Final project

First of all, we set a seed for reproducibility of the results.

# Set the seed for random number generation to ensure reproducibility of results
set.seed(42)

Preprocessing and EDA

Goal

We are astronomy researchers in a private company that works by founding and we are in charge of categorizing observed space objects as either stars, galaxies or quasar objects (QSO). We have a dataset that provides us with 100000 observations of space taken by the SDSS (Sloan Digital Sky Survey). Our classification task is very important because we are later going to analyse the objects and depending on what they are, we are going to receive more or less money for its study (more for quasar objects). Hence, we are going to focus on the variable calls that tells us the object class out of the three mentioned possible ones.

We are in charge of creating a report with the number of each of this objects to submit at the end of the month in order to receive the corresponding funding the following month, that is why we are interested in predicting the class of these objects to be able to ask for the amount of money we need and not end up with more or less of what we need, because if we end with less we can’t carry out every test we need to study the object, and if we receive more we will have a penalty on next month funding.

As we said, the data consists of 100000 observations of space objects taken by the SDSS (Sloan Digital Sky Survey). Every observation is described by 17 feature columns and 1 class column which identifies it to be either a star, galaxy or quasar (our target variable).

In summary, we are going to use the variables in our dataset to predict the variable class, which we are going to transform into a binary variable with levels ‘QSO’ and ‘NOT.QSO’. We are going to use the machine learning methods seen in class (k-nearest neighbours, support vector machines, decision trees, random forest, gradient boosting neural networks and deep neural networks).

Load the datasets (review)

First of all, we load our dataset.

data_whole <- read.csv("star_classification.csv")
head(data_whole)

##         obj_ID    alpha     delta        u        g        r        i        z
## 1 1.237679e+18 341.4055  8.578359 19.92760 18.16381 17.06835 16.56405 16.16184
## 2 1.237679e+18 357.7307 11.390703 21.31802 21.14918 21.06537 20.85919 20.71210
## 3 1.237659e+18 246.1227 35.108206 20.76552 20.74977 20.81548 20.50528 20.86766
## 4 1.237661e+18 197.5328 48.984102 23.29459 21.82885 20.12599 19.10921 18.81542
## 5 1.237662e+18 195.3274 42.810901 22.92567 21.65787 20.03059 19.57382 19.39124
## 6 1.237658e+18 126.9168 30.977271 19.47264 17.61016 16.82478 16.52191 16.32555
##   run_ID rerun_ID cam_col field_ID  spec_obj_ID  class     redshift plate   MJD
## 1   7808      301       2       84 5.696147e+18 GALAXY 0.1418661000  5059 56190
## 2   7773      301       1      295 1.299978e+19    QSO 1.9429430000 11546 58488
## 3   3185      301       4       39 1.207754e+19    QSO 1.8896200000 10727 58197
## 4   3650      301       3       64 7.603369e+18 GALAXY 0.5099247000  6753 56399
## 5   3893      301       5      226 1.641713e+18 GALAXY 0.4178358000  1458 53119
## 6   2963      301       1       29 3.582649e+18   STAR 0.0001985597  3182 54828
##   fiber_ID
## 1      798
## 2      506
## 3       38
## 4      607
## 5      549
## 6      128

We can see that we have the following 18 variables:

• obj_ID: Object Identifier, the unique value that identifies the object in the image catalog used by the CAS.
• alpha: Right Ascension angle (at J2000 epoch).
• delta: Declination angle (at J2000 epoch).
• u: Ultraviolet filter in the photometric system.
• g: Green filter in the photometric system.
• r: Red filter in the photometric system.
• i: Near Infrared filter in the photometric system.
• z: Infrared filter in the photometric system.
• run_ID: Run Number used to identify the specific scan.
• rereun_ID: Rerun Number to specify how the image was processed.
• cam_col: Camera column to identify the scanline within the run.
• field_ID: Field number to identify each field.
• spec_obj_ID: Unique ID used for optical spectroscopic objects (this means that 2 different observations with the same spec_obj_ID must share the output class).
• class: Object class (galaxy, star or quasar object).
• redshift: Redshift value based on the increase in wavelength.
• plate: Plate ID, identifies each plate in SDSS.
• MJD: Modified Julian Date, used to indicate when a given piece of SDSS data was taken.
• fiber_ID: Fiber ID that identifies the fiber that pointed the light at the focal plane in each observation.

We have 14 numerical variables (obj_ID, alpha, delta, u, g, r, i, z, run_ID, rerun_ID, redshift, field_ID, spec_obj_ID and fiber_ID), 3 integer variables (cam_col, plate and MJD) and one multicategorical, our target variable class.

Eliminate unnecesary variables and various modifications

First of all, we are going to eliminate the variable obj_ID because it is an individual identification number that does not provide any information. We are also going to eliminate the rerun_ID variable because it is constant with value 301 in all the observations.

In addition, we are going to make the run_ID variable multicategorical by creating categories for the numbers every 1000. In other words, the observations with values in this variable between 0 and 1000 are all part of the ‘0-1000’ category, the ones with value between 1001 and 2000 are in the category 1000-2000, and so on. Afterwards, we are going to set all the categorical variables (class, run_ID and cam_col) as factors.

# Eliminate the specified variables from the dataset
data = data_whole[,-c(1, 10)]

# Make multicategorical variables
# Define the breaks for categorization
breaks <- seq(0, 9000, by = 1000)

# Create a new categorical variable based on 'run_ID'
data$run_ID <- cut(data$run_ID, breaks = breaks, labels = paste(breaks[-length(breaks)], "-", breaks[-1]))

# Convert categorical variables to factor type
data[, c('class', 'run_ID', 'cam_col')] <- lapply(data[, c('class', 'run_ID', 'cam_col')], function(x) as.factor(x))

# Display the structure and first few rows of the preprocessed dataset
head(data)

##      alpha     delta        u        g        r        i        z      run_ID
## 1 341.4055  8.578359 19.92760 18.16381 17.06835 16.56405 16.16184 7000 - 8000
## 2 357.7307 11.390703 21.31802 21.14918 21.06537 20.85919 20.71210 7000 - 8000
## 3 246.1227 35.108206 20.76552 20.74977 20.81548 20.50528 20.86766 3000 - 4000
## 4 197.5328 48.984102 23.29459 21.82885 20.12599 19.10921 18.81542 3000 - 4000
## 5 195.3274 42.810901 22.92567 21.65787 20.03059 19.57382 19.39124 3000 - 4000
## 6 126.9168 30.977271 19.47264 17.61016 16.82478 16.52191 16.32555 2000 - 3000
##   cam_col field_ID  spec_obj_ID  class     redshift plate   MJD fiber_ID
## 1       2       84 5.696147e+18 GALAXY 0.1418661000  5059 56190      798
## 2       1      295 1.299978e+19    QSO 1.9429430000 11546 58488      506
## 3       4       39 1.207754e+19    QSO 1.8896200000 10727 58197       38
## 4       3       64 7.603369e+18 GALAXY 0.5099247000  6753 56399      607
## 5       5      226 1.641713e+18 GALAXY 0.4178358000  1458 53119      549
## 6       1       29 3.582649e+18   STAR 0.0001985597  3182 54828      128

Prepare the target variable

As we mentioned, we are going to make our target variable binary by combining the ‘Star’ and ‘Galaxy’ categories into a level called ‘NOT.QSO’.

# Recode the levels of the class variable
data$class <- factor(data$class, levels = c("STAR", "GALAXY", "QSO"))

# Create a new binary variable with custom labels
data$class <- factor(ifelse(data$class %in% c("STAR", "GALAXY"), "NOT.QSO", "QSO"), levels = c("NOT.QSO", "QSO"))

Missing values

Now we wanna study the variables and to do that we compute a summary to see some details like the mean, median, minimum, maximum, missing values (NA’s),…

# Summarize the data frame
summary(data)

##      alpha              delta               u                  g           
##  Min.   :  0.0298   Min.   :-18.785   Min.   :-9999.00   Min.   :-9999.00  
##  1st Qu.:126.4262   1st Qu.:  4.612   1st Qu.:   20.37   1st Qu.:   18.99  
##  Median :177.6281   Median : 23.740   Median :   22.18   Median :   21.14  
##  Mean   :174.7733   Mean   : 24.126   Mean   :   21.09   Mean   :   19.57  
##  3rd Qu.:231.6237   3rd Qu.: 40.398   3rd Qu.:   23.69   3rd Qu.:   22.13  
##  Max.   :359.9970   Max.   : 82.757   Max.   :   29.23   Max.   :   29.86  
##                     NA's   :935                          NA's   :747       
##        r                i               z                    run_ID    
##  Min.   : 9.822   Min.   : 9.47   Min.   :-9999.00   3000 - 4000:2383  
##  1st Qu.:18.201   1st Qu.:17.80   1st Qu.:   17.52   4000 - 5000:1900  
##  Median :20.160   Median :19.41   Median :   19.00   2000 - 3000:1522  
##  Mean   :19.660   Mean   :19.10   Mean   :   17.79   5000 - 6000:1289  
##  3rd Qu.:21.037   3rd Qu.:20.44   3rd Qu.:   19.94   7000 - 8000:1207  
##  Max.   :29.572   Max.   :32.14   Max.   :   28.79   1000 - 2000: 795  
##  NA's   :932                                         (Other)    : 904  
##  cam_col     field_ID      spec_obj_ID            class     
##  1:1357   Min.   : 11.0   Min.   :3.006e+17   NOT.QSO:8083  
##  2:1730   1st Qu.: 83.0   1st Qu.:2.880e+18   QSO    :1917  
##  3:1911   Median :148.0   Median :5.631e+18                 
##  4:1930   Mean   :186.7   Mean   :5.824e+18                 
##  5:1823   3rd Qu.:240.0   3rd Qu.:8.344e+18                 
##  6:1249   Max.   :980.0   Max.   :1.413e+19                 
##                                                             
##     redshift             plate            MJD           fiber_ID     
##  Min.   :-0.004136   Min.   :  267   Min.   :51608   Min.   :   1.0  
##  1st Qu.: 0.050320   1st Qu.: 2558   1st Qu.:54270   1st Qu.: 216.0  
##  Median : 0.426655   Median : 5001   Median :55896   Median : 431.0  
##  Mean   : 0.578574   Mean   : 5173   Mean   :55607   Mean   : 446.9  
##  3rd Qu.: 0.707591   3rd Qu.: 7411   3rd Qu.:56799   3rd Qu.: 641.0  
##  Max.   : 7.010272   Max.   :12547   Max.   :58932   Max.   :1000.0  
##                                      NA's   :761

We see that in the variables u, g and z something is up because the minimum is -9999, which can not be. If we look at the dataset we can see that the observations with those values in those variables are the same one, so we are going to eliminate said observation.

which(data$u == -9999)

## [1] 2045

which(data$g == -9999)

## [1] 2045

which(data$z == -9999)

## [1] 2045

As we said, we can see that it is the same observation (observation 2045), so we eliminate it from our dataset.

data = data[-2045, ]

Now, we compute the summary again and we see that nothing of the sorts happens in any other variable and that it still does not happen in the ones mentioned.

summary(data)

##      alpha              delta               u               g        
##  Min.   :  0.0298   Min.   :-18.785   Min.   :14.15   Min.   :10.73  
##  1st Qu.:126.4219   1st Qu.:  4.615   1st Qu.:20.37   1st Qu.:18.99  
##  Median :177.6225   Median : 23.746   Median :22.18   Median :21.14  
##  Mean   :174.7684   Mean   : 24.129   Mean   :22.09   Mean   :20.65  
##  3rd Qu.:231.6335   3rd Qu.: 40.400   3rd Qu.:23.69   3rd Qu.:22.13  
##  Max.   :359.9970   Max.   : 82.757   Max.   :29.23   Max.   :29.86  
##                     NA's   :935                       NA's   :747    
##        r                i               z                  run_ID     cam_col 
##  Min.   : 9.822   Min.   : 9.47   Min.   : 9.612   3000 - 4000:2383   1:1357  
##  1st Qu.:18.201   1st Qu.:17.80   1st Qu.:17.521   4000 - 5000:1900   2:1729  
##  Median :20.160   Median :19.41   Median :19.005   2000 - 3000:1522   3:1911  
##  Mean   :19.660   Mean   :19.10   Mean   :18.790   5000 - 6000:1289   4:1930  
##  3rd Qu.:21.037   3rd Qu.:20.44   3rd Qu.:19.942   7000 - 8000:1207   5:1823  
##  Max.   :29.572   Max.   :32.14   Max.   :28.791   1000 - 2000: 795   6:1249  
##  NA's   :932                                       (Other)    : 903           
##     field_ID      spec_obj_ID            class         redshift        
##  Min.   : 11.0   Min.   :3.006e+17   NOT.QSO:8082   Min.   :-0.004136  
##  1st Qu.: 83.0   1st Qu.:2.880e+18   QSO    :1917   1st Qu.: 0.050434  
##  Median :148.0   Median :5.631e+18                  Median : 0.426959  
##  Mean   :186.6   Mean   :5.824e+18                  Mean   : 0.578632  
##  3rd Qu.:240.0   3rd Qu.:8.344e+18                  3rd Qu.: 0.707636  
##  Max.   :980.0   Max.   :1.413e+19                  Max.   : 7.010272  
##                                                                        
##      plate            MJD           fiber_ID     
##  Min.   :  267   Min.   :51608   Min.   :   1.0  
##  1st Qu.: 2558   1st Qu.:54270   1st Qu.: 216.5  
##  Median : 5001   Median :55896   Median : 431.0  
##  Mean   : 5173   Mean   :55607   Mean   : 447.0  
##  3rd Qu.: 7411   3rd Qu.:56799   3rd Qu.: 641.0  
##  Max.   :12547   Max.   :58932   Max.   :1000.0  
##                  NA's   :761

We are interested in dealing with the missing values and getting rid of them in the best way we can. We compute the percentage of observations with missing values in each variable.

# Compute the percentage of missing values for each variable
sapply(data, function(x) mean(is.na(x)) * 100)

##       alpha       delta           u           g           r           i 
##    0.000000    9.350935    0.000000    7.470747    9.320932    0.000000 
##           z      run_ID     cam_col    field_ID spec_obj_ID       class 
##    0.000000    0.000000    0.000000    0.000000    0.000000    0.000000 
##    redshift       plate         MJD    fiber_ID 
##    0.000000    0.000000    7.610761    0.000000

We can see that we have missing values in the variables delta, g, r and MJD. However, we do not eliminate these variables, instead what we do is compute the missing values. We decide not to eliminate them because the percentage of missing values is very low in each variable: delta has 9.35%, g has 7.47%, r has 9.32% and MJD has 7.61%.

The missing values are all in numeric variables, so we want to see the distributions of the data and if they have outliers, so we compute the histograms (to see skewness and symmetry) and boxplots (to see outliers) of each variable. If the variable is normally distributed or approximately symmetric without extreme outliers we exchange the NA’s with the mean of the variable. However, if it is heavily skewed or with outliers, we substitute it with the median.

# Take only the variables with missing values
missing_vars = c("delta", "g", "r", "MJD")

# Create histograms for symmetry and skewness of each variable
for(var in missing_vars) {
  # Plot histogram for the current variable
  hist(data[[var]], 
       main = paste("Histogram of", var),    # Title of the plot
       xlab = var,                           # Label for x-axis
       ylab = "Frequency",                   # Label for y-axis
       col = "skyblue",                      # Fill color of bars
       border = "black"                      # Border color of bars
  )
}

We also compute the skewness.

selected_data <- data_whole[, missing_vars]
apply(selected_data, 2, skewness, na.rm = TRUE)

##       delta           g           r         MJD 
##   0.1665573 -96.1064511  -0.5321120  -0.3903255

Such strong skewness on the variable g suggest the presence of many extreme outliers. And now the boxplots to check for outliers. We compute the ones corresponding to the variables delta, ag and r separate from the one corresponding to MJD because of the scaling and, hence, to visualize it better.

# Extract the columns with missing values from the dataset
missing = setdiff(missing_vars, 'MJD')
  
missing = data[,missing]

# Create boxplots to identify outliers in each variable
boxplot(missing,                       # Data for boxplot
        outline = TRUE,                # Show outliers
        col = 'skyblue',               # Fill color of boxes
        border = 'black'               # Border color of boxes
)

# Create boxplot to identify outliers in MJD
boxplot(data[, 'MJD' ],                       # Data for boxplot
        outline = TRUE,                # Show outliers
        col = 'skyblue',               # Fill color of boxes
        border = 'black',               # Border color of boxes
        xlab = 'MJD'
)

We can see, following the criteria we said before, that we have to use the mean in the variable delta, and the median in the variables g, r and MJD.

# Replace missing values with mean for delta
data$delta = replace_na(data$delta, median(data$delta, na.rm=TRUE))

# Replace missing values with median for g
data$g = replace_na(data$g, median(data$g, na.rm=TRUE))

# Replace missing values with median for r
data$r = replace_na(data$r, median(data$r, na.rm=TRUE))

# Replace missing values with median for MJD
data$MJD = replace_na(data$MJD, median(data$MJD, na.rm=TRUE))

We check that there are no remaining missing values with the following function.

# Function to check for missing values
check_missing_values <- function(data) {
  if (any(is.na(data))) {          # Check if any missing values exist
    message("There are missing values left.")  # Print a message if missing values are found
  } else {
    message("There are no missing values left.")  # Print a message if no missing values are found
  }
}

# Call the function with our dataset
check_missing_values(data)  # Check missing values in data dataset

## There are no missing values left.

Observe the target variable:

We can see in the following code that the classes are not well balanced, they are, in fact, extremely unbalanced (80.82% NOT.QSO and 19.18% QSO).

# Display the frequency table of the 'temp' variable
table(data$class)

## 
## NOT.QSO     QSO 
##    8082    1917

Train/test data split

After creating classifiers and training them, we will want to see how well they perform, how accurate they are. To do this, we need to do a train/test partition. We do this so we can use the train partition to fit the model and the test partition to measure its performance. We make a partition with 40% on the training and 60% on the testing because of the high volume of observations we have.

The train/test partition helps to prevent overfitting, which occurs when a model learns the training data too well and performs poorly on new data. By evaluating the model on a separate testing set, you can get a more accurate estimate of its performance on unseen data.

# Shuffle the rows of the dataset
shuffled_data <- data[sample(nrow(data)), ]

# Create a train/test partition with the shuffled data
train_index <- createDataPartition(
  y = shuffled_data$class,  # Specify the response variable for stratified sampling
  p = 0.4,                 # Proportion of data for the training set, 40%
  list = FALSE             # Return indices as a vector, not as a list
)

# Create the training and testing datasets
train_data <- shuffled_data[train_index, ]  # Subset of shuffled_data for training
test_data <- shuffled_data[-train_index, ]  # Subset of shuffled_data for testing

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is an approach to analyzing datasets in order to summarize their main characteristics, often using visual methods. Now that we have done the partition, we are going to carry this kind of analysis on the train dataset.

Univariate analysis

Here we observe the distributions of the numerical variables via a histogram of frequency for each variable to see their distributions and have an idea about them. We separate it by their values on the variable class.

# Define numeric variables
numeric_vars <- c('alpha', 'delta', 'u', 'g', 'r', 'i', 'z', 'field_ID', 'spec_obj_ID', 'redshift', 'plate', 'MJD', 'fiber_ID')

# Create histograms for each numeric variable
for (var in numeric_vars) {
  # Create a ggplot object
  hist_plot <- ggplot(train_data, aes(x = !!sym(var), fill = class)) +
    geom_histogram(bins = 30, alpha = 0.5, position = "identity") +
    labs(title = paste("Histogram of", var),
         x = var,
         y = "Frequency") +
    scale_fill_manual(values = c("skyblue", "salmon"))  # Define colors for classes
  
  # Print the histogram
  print(hist_plot)
}

We can see that in the variable u, non quasar objects take more extreme values than quasar objects. If an object has an observation in this variable that is less than 17 or more than 27, then it is a non quasar object. Moreover, in the variable delta we can see that both classes take the same range of values, they differ in frequency but because of the unbalance in the number of observations. In the variable i, we can also see that those observations with values lower than 17 are non quasar objects and with more than 17 can be both.

Now, we are going to study the other variables, the categorical ones. We see how the observations in our categorical variables are distributed via barplots for each variable.

# Define categorical variables
categorical_vars <- c('run_ID', 'cam_col', 'class')

# Create bar plots for each categorical variable in the training dataset
for(var in categorical_vars) {
  # Compute frequency table for the current categorical variable
  freq_table <- table(train_data[[var]])
  
  # Create a bar plot
  barplot(freq_table,                  # Data for bar plot
           main = paste("Bar plot of", var),  # Title of the bar plot
           xlab = var,                  # Label for x-axis
           ylab = "Frequency",          # Label for y-axis
           col = "skyblue",             # Fill color of bars
           border = "black"             # Border color of bars
  )
}

In the barplot of our target variable class, we can once again see the unbalance of our two categories. If we observe a new object and make a prediction based on this barplot, we would classify all of them as ‘NOT.QSO’. This is what a naive classifier does, classify on the more numerous class.

Bivariate analysis

We are going to explore some of the combinations of variables by pairs of numeric variables to see the relationship between them, but also their relationship with the target variable. I think it is important to see these plots, so we can see that we can observe other things not involving our target variable. For example, delta and `redshift``.

# Scatter plot using ggplot2
ggplot(train_data, aes(x = delta, y = redshift, color = class)) +  # Specify the data and aesthetics
  geom_point(alpha = 0.5) +           # Add points with transparency and color
  labs(title = "Scatter plot of delta vs. redshift",  # Set the title and axis labels
       x = "Delta", 
       y = "Redshift") + 
  theme(plot.title = element_text(hjust = 0.5))           # Adjust the title position

we can see that, it does not follow any pattern. It does not look like there is any correlation between the two variables. All values of delta take all values of redshift. However, what we can observe is that the non quasar objects take lower values in the redshift variable, the higher values are only taken by the quasar objects.

If we think about it, two variables that seem reasonable that will be correlated are r and g, because both are related to colour filters in the photometric system. We expect to observe a pattern in the plot.

# Scatter plot using ggplot2
ggplot(train_data, aes(x = r, y = g, color = class)) +  # Specify the data and aesthetics
  geom_point(alpha = 0.5) +           # Add points with transparency and color
  labs(title = "Scatter plot of r vs. g",  # Set the title and axis labels
       x = "r", 
       y = "g") + 
  theme(plot.title = element_text(hjust = 0.5))           # Adjust the title position

We, in fact, do observe a pattern that indicates high correlation. However, we have some observations that do not follow the pattern, which show that while we have high correlation, we do not have extremely high correlation. We will see it in the correlation matrix we will later compute with the corresponding value being high, but not extremely close to 1. Here, we can not draw any conclusion related to our target variable because the distribution is similar.

Another pair of variables that seems reasonable to think that will have this high correlation are u and g because of the same reason, both are related to the colour filter in the photometric system.

# Scatter plot using ggplot2
ggplot(train_data, aes(x = r, y = i, color = class)) +  # Specify the data and aesthetics
  geom_point(alpha = 0.5) +           # Add points with transparency and color
  labs(title = "Scatter plot of r vs. i",  # Set the title and axis labels
       x = "r", 
       y = "i") + 
  theme(plot.title = element_text(hjust = 0.5))           # Adjust the title position

We observe the pattern we expected, one that supports our idea of the variables being highly correlated, but not perfectly. Once again, we can not draw any conclusion on our target variable from this plot.

Another pair of variables that seems interesting to plot are alpha and deltaand we will in fact observe some clusters, but we will not be able to draw any conclusion on our target variable.

# Scatter plot using ggplot2
ggplot(train_data, aes(x = alpha, y = delta, color = class)) +  # Specify the data and aesthetics
  geom_point(alpha = 0.5) +       # Add points with transparency and color
  labs(title = "Scatter plot of alpha vs. delta",  # Set the title and axis labels
       x = "Alpha", 
       y = "Delta") + 
  theme(plot.title = element_text(hjust = 0.5))       # Adjust the title position

We are now going to use boxplots to observe other pairs of variables where at least one will be our target variable (we use boxplots because class is categorical and they are more informative with these variables). For example, it may be interesting to see the relation between class and redshift.

# Boxplot using ggplot2
ggplot(train_data, aes(x = class, y = redshift)) +  # Specify the data and aesthetics
  geom_boxplot(fill = "skyblue", color = "black") +   # Add boxplots with fill and border color
  labs(title = "Box plot of redshift by class",  # Set the title and axis labels
       x = "Class", 
       y = "Redshift") + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5))  # Adjust the title font and position

Here, we can clearly see that the bigger values (more than 1) of redshift correspond to observations of quasar objects (as we saw in the first scatterplot). Let’s see if we can observe something similar with other variables and our target variable. For example, class with i.

# Boxplot using ggplot2
ggplot(train_data, aes(x = class, y = i)) +      # Specify the data and aesthetics
  geom_boxplot(fill = "skyblue", color = "black") + # Add boxplots with fill and border color
  labs(title = "Box plot of i by class",  # Set the title and axis labels
       x = "Class", 
       y = "i") + 
  theme(plot.title = element_text(face = "bold", hjust = 0.5))  # Adjust the title font and position

Here, we can see that values of i bigger than 20 correspond to QSO and lower values correspond to NOT.QSO. This is something that we were not able to see in the scatterplot.

As a final bivariate analysis, we are going to compute the correlation matrix, but only of the numeric variables. Here, we will be able to check if our conclusion drawn from plots are true or not about the correlation of the observed matrices.

# Compute the correlation matrix for numeric variables in the training dataset
correlation_matrix <- cor(train_data[, numeric_vars])
correlation_matrix

##                     alpha         delta           u            g            r
## alpha        1.0000000000  0.1362838132  0.02559222 -0.005500065 -0.006899563
## delta        0.1362838132  1.0000000000 -0.02216318 -0.002474210 -0.006307691
## u            0.0255922244 -0.0221631829  1.00000000  0.822362714  0.690649355
## g           -0.0055000647 -0.0024742100  0.82236271  1.000000000  0.854288479
## r           -0.0068995630 -0.0063076906  0.69064936  0.854288479  1.000000000
## i           -0.0175212355 -0.0003301599  0.60871598  0.811336104  0.914781634
## z           -0.0185645409 -0.0028896333  0.54142889  0.751061711  0.878134319
## field_ID    -0.1285622311 -0.1468639545 -0.03863537 -0.047378504 -0.043615142
## spec_obj_ID -0.0239720944  0.0804291749  0.37730968  0.552109899  0.611419472
## redshift    -0.0002882211 -0.0056673573  0.16034643  0.296420783  0.408272583
## plate       -0.0239735297  0.0804292969  0.37730805  0.552108688  0.611418224
## MJD         -0.0032040014  0.0765470790  0.40026700  0.552483401  0.601216456
## fiber_ID     0.0584600427  0.0143624941  0.16648555  0.190467585  0.206764500
##                         i            z    field_ID spec_obj_ID      redshift
## alpha       -0.0175212355 -0.018564541 -0.12856223 -0.02397209 -0.0002882211
## delta       -0.0003301599 -0.002889633 -0.14686395  0.08042917 -0.0056673573
## u            0.6087159751  0.541428889 -0.03863537  0.37730968  0.1603464271
## g            0.8113361036  0.751061711 -0.04737850  0.55210990  0.2964207826
## r            0.9147816336  0.878134319 -0.04361514  0.61141947  0.4082725833
## i            1.0000000000  0.973451958 -0.03770986  0.65606604  0.4832415491
## z            0.9734519584  1.000000000 -0.03814630  0.64821589  0.4912012449
## field_ID    -0.0377098631 -0.038146304  1.00000000 -0.06314366 -0.0161698096
## spec_obj_ID  0.6560660386  0.648215891 -0.06314366  1.00000000  0.3869796667
## redshift     0.4832415491  0.491201245 -0.01616981  0.38697967  1.0000000000
## plate        0.6560648677  0.648214883 -0.06314376  1.00000000  0.3869785409
## MJD          0.6381283714  0.628586074 -0.06246765  0.92723968  0.3702241153
## fiber_ID     0.2143259621  0.205066962 -0.01096934  0.24666142  0.1459373850
##                   plate          MJD    fiber_ID
## alpha       -0.02397353 -0.003204001  0.05846004
## delta        0.08042930  0.076547079  0.01436249
## u            0.37730805  0.400267003  0.16648555
## g            0.55210869  0.552483401  0.19046758
## r            0.61141822  0.601216456  0.20676450
## i            0.65606487  0.638128371  0.21432596
## z            0.64821488  0.628586074  0.20506696
## field_ID    -0.06314376 -0.062467646 -0.01096934
## spec_obj_ID  1.00000000  0.927239675  0.24666142
## redshift     0.38697854  0.370224115  0.14593738
## plate        1.00000000  0.927239149  0.24664049
## MJD          0.92723915  1.000000000  0.25226180
## fiber_ID     0.24664049  0.252261798  1.00000000

Here, we can corroborate what we showed before: r and g are highly correlated (0.84), as well as r and i (0.92); while delta and redshift are not (0.006), as well as alpha and beta (0.17).

Multivariate analysis

Now, we are going to compute a heat map of the previously computed correlation matrix.

# Convert correlation matrix to a matrix
cor_matrix <- as.matrix(correlation_matrix)

# Create the correlation heatmap
heatmap(cor_matrix, 
        Rowv = NA,         # Do not reorder rows
        Colv = NA,         # Do not reorder columns
        col = colorRampPalette(c("blue", "white", "red"))(100),  # Define color scheme
        scale = "none",    # Do not scale rows or columns
        main = "Correlation heatmap of numeric variables"        # Set the title of the plot
)

# Add color legend
legend("bottomright", 
       legend = c("Low", "Medium", "High"),                     # Legend labels
       fill = colorRampPalette(c("blue", "white", "red"))(3),   # Colors corresponding to legend labels
       title = "Correlation",                                   # Title of the legend
       bg = "transparent"                                       # Transparent background
)

We can observe once again that r and g are highly correlated, as well as r and i, but delta and redshift are not and neither alpha and beta. On top of that, we can see how correlated are each and every pair of variables. As the legend says, the redder the square, the more correlated they are and the bluest, the lowest correlation.

Machine Learning classifiers

The Benchmark

A benchmark model serves as a baseline for comparison when developing or evaluating more complex models. It’s typically a simple, easy-to-understand model that provides a reference point for assessing the performance of more sophisticated algorithms.

The purpose of a benchmark model is to establish a minimum level of performance that any new model must surpass to be considered useful. It helps in gauging whether the additional complexity of a more advanced model is justified by a significant improvement in performance. Additionally, benchmark models are useful for understanding the difficulty of the task and for providing a point of comparison across different approaches or datasets.

Since we have many predictors, we saw in class that a good benchmark will be penalized logistic regression. Penalized logistic regression is a modification of logistic regression that incorporates regularization techniques to prevent overfitting and improve generalization performance. It introduces penalty terms to the loss function, discouraging large coefficient values. It addresses issues such as multicollinearity, overfitting, and high dimensionality.

Firstly, we need the control function which implements cross-validation with 5 folds.

ctrllog <- trainControl(method = "cv", number = 5,
                     classProbs = TRUE, 
                     verboseIter=T)

Now, we can fit our model using the caret package and incorporating our control function. Moreover, we take Kappa as our accuracy metric.

# Train the logistic regression model using penalized logistic regression (glmnet)
lrFit <- train(class ~ .,                     # Predict class based on all predictors
               method = "glmnet",             # Use glmnet method for logistic regression
               tuneGrid = expand.grid(alpha = seq(0, 1, 0.1), lambda = seq(0, .1, 0.02)),  # Hyperparameter grid for alpha and lambda
               metric = "Kappa",             # Use Kappa as the evaluation metric
               data = train_data,            # Training data
               preProcess = c("center", "scale"),  # Preprocess data by centering and scaling
               trControl = ctrllog)          # Use defined control object for cross-validation

## + Fold1: alpha=0.0, lambda=0.1 
## - Fold1: alpha=0.0, lambda=0.1 
## + Fold1: alpha=0.1, lambda=0.1 
## - Fold1: alpha=0.1, lambda=0.1 
## + Fold1: alpha=0.2, lambda=0.1 
## - Fold1: alpha=0.2, lambda=0.1 
## + Fold1: alpha=0.3, lambda=0.1 
## - Fold1: alpha=0.3, lambda=0.1 
## + Fold1: alpha=0.4, lambda=0.1 
## - Fold1: alpha=0.4, lambda=0.1 
## + Fold1: alpha=0.5, lambda=0.1 
## - Fold1: alpha=0.5, lambda=0.1 
## + Fold1: alpha=0.6, lambda=0.1 
## - Fold1: alpha=0.6, lambda=0.1 
## + Fold1: alpha=0.7, lambda=0.1 
## - Fold1: alpha=0.7, lambda=0.1 
## + Fold1: alpha=0.8, lambda=0.1 
## - Fold1: alpha=0.8, lambda=0.1 
## + Fold1: alpha=0.9, lambda=0.1 
## - Fold1: alpha=0.9, lambda=0.1 
## + Fold1: alpha=1.0, lambda=0.1 
## - Fold1: alpha=1.0, lambda=0.1 
## + Fold2: alpha=0.0, lambda=0.1 
## - Fold2: alpha=0.0, lambda=0.1 
## + Fold2: alpha=0.1, lambda=0.1 
## - Fold2: alpha=0.1, lambda=0.1 
## + Fold2: alpha=0.2, lambda=0.1 
## - Fold2: alpha=0.2, lambda=0.1 
## + Fold2: alpha=0.3, lambda=0.1 
## - Fold2: alpha=0.3, lambda=0.1 
## + Fold2: alpha=0.4, lambda=0.1 
## - Fold2: alpha=0.4, lambda=0.1 
## + Fold2: alpha=0.5, lambda=0.1 
## - Fold2: alpha=0.5, lambda=0.1 
## + Fold2: alpha=0.6, lambda=0.1 
## - Fold2: alpha=0.6, lambda=0.1 
## + Fold2: alpha=0.7, lambda=0.1 
## - Fold2: alpha=0.7, lambda=0.1 
## + Fold2: alpha=0.8, lambda=0.1 
## - Fold2: alpha=0.8, lambda=0.1 
## + Fold2: alpha=0.9, lambda=0.1 
## - Fold2: alpha=0.9, lambda=0.1 
## + Fold2: alpha=1.0, lambda=0.1 
## - Fold2: alpha=1.0, lambda=0.1 
## + Fold3: alpha=0.0, lambda=0.1 
## - Fold3: alpha=0.0, lambda=0.1 
## + Fold3: alpha=0.1, lambda=0.1 
## - Fold3: alpha=0.1, lambda=0.1 
## + Fold3: alpha=0.2, lambda=0.1 
## - Fold3: alpha=0.2, lambda=0.1 
## + Fold3: alpha=0.3, lambda=0.1 
## - Fold3: alpha=0.3, lambda=0.1 
## + Fold3: alpha=0.4, lambda=0.1 
## - Fold3: alpha=0.4, lambda=0.1 
## + Fold3: alpha=0.5, lambda=0.1 
## - Fold3: alpha=0.5, lambda=0.1 
## + Fold3: alpha=0.6, lambda=0.1 
## - Fold3: alpha=0.6, lambda=0.1 
## + Fold3: alpha=0.7, lambda=0.1 
## - Fold3: alpha=0.7, lambda=0.1 
## + Fold3: alpha=0.8, lambda=0.1 
## - Fold3: alpha=0.8, lambda=0.1 
## + Fold3: alpha=0.9, lambda=0.1 
## - Fold3: alpha=0.9, lambda=0.1 
## + Fold3: alpha=1.0, lambda=0.1 
## - Fold3: alpha=1.0, lambda=0.1 
## + Fold4: alpha=0.0, lambda=0.1 
## - Fold4: alpha=0.0, lambda=0.1 
## + Fold4: alpha=0.1, lambda=0.1 
## - Fold4: alpha=0.1, lambda=0.1 
## + Fold4: alpha=0.2, lambda=0.1 
## - Fold4: alpha=0.2, lambda=0.1 
## + Fold4: alpha=0.3, lambda=0.1 
## - Fold4: alpha=0.3, lambda=0.1 
## + Fold4: alpha=0.4, lambda=0.1 
## - Fold4: alpha=0.4, lambda=0.1 
## + Fold4: alpha=0.5, lambda=0.1 
## - Fold4: alpha=0.5, lambda=0.1 
## + Fold4: alpha=0.6, lambda=0.1 
## - Fold4: alpha=0.6, lambda=0.1 
## + Fold4: alpha=0.7, lambda=0.1 
## - Fold4: alpha=0.7, lambda=0.1 
## + Fold4: alpha=0.8, lambda=0.1 
## - Fold4: alpha=0.8, lambda=0.1 
## + Fold4: alpha=0.9, lambda=0.1 
## - Fold4: alpha=0.9, lambda=0.1 
## + Fold4: alpha=1.0, lambda=0.1 
## - Fold4: alpha=1.0, lambda=0.1 
## + Fold5: alpha=0.0, lambda=0.1 
## - Fold5: alpha=0.0, lambda=0.1 
## + Fold5: alpha=0.1, lambda=0.1 
## - Fold5: alpha=0.1, lambda=0.1 
## + Fold5: alpha=0.2, lambda=0.1 
## - Fold5: alpha=0.2, lambda=0.1 
## + Fold5: alpha=0.3, lambda=0.1 
## - Fold5: alpha=0.3, lambda=0.1 
## + Fold5: alpha=0.4, lambda=0.1 
## - Fold5: alpha=0.4, lambda=0.1 
## + Fold5: alpha=0.5, lambda=0.1 
## - Fold5: alpha=0.5, lambda=0.1 
## + Fold5: alpha=0.6, lambda=0.1 
## - Fold5: alpha=0.6, lambda=0.1 
## + Fold5: alpha=0.7, lambda=0.1 
## - Fold5: alpha=0.7, lambda=0.1 
## + Fold5: alpha=0.8, lambda=0.1 
## - Fold5: alpha=0.8, lambda=0.1 
## + Fold5: alpha=0.9, lambda=0.1 
## - Fold5: alpha=0.9, lambda=0.1 
## + Fold5: alpha=1.0, lambda=0.1 
## - Fold5: alpha=1.0, lambda=0.1 
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.7, lambda = 0 on full training set

print(lrFit)                                # Print the trained logistic regression model

## glmnet 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3199, 3199, 3201, 3200, 3201 
## Resampling results across tuning parameters:
## 
##   alpha  lambda  Accuracy   Kappa    
##   0.0    0.00    0.9477540  0.8150916
##   0.0    0.02    0.9477540  0.8150916
##   0.0    0.04    0.9447531  0.8031191
##   0.0    0.06    0.9395005  0.7812586
##   0.0    0.08    0.9344993  0.7594690
##   0.0    0.10    0.9287493  0.7345168
##   0.1    0.00    0.9637521  0.8786753
##   0.1    0.02    0.9500034  0.8245278
##   0.1    0.04    0.9462515  0.8082264
##   0.1    0.06    0.9400009  0.7822828
##   0.1    0.08    0.9292496  0.7372424
##   0.1    0.10    0.9215030  0.7018912
##   0.2    0.00    0.9635018  0.8779057
##   0.2    0.02    0.9495027  0.8225241
##   0.2    0.04    0.9460009  0.8065513
##   0.2    0.06    0.9367509  0.7690644
##   0.2    0.08    0.9260021  0.7227709
##   0.2    0.10    0.9152527  0.6723897
##   0.3    0.00    0.9637515  0.8788291
##   0.3    0.02    0.9500018  0.8243332
##   0.3    0.04    0.9445027  0.8006202
##   0.3    0.06    0.9340009  0.7572600
##   0.3    0.08    0.9242508  0.7138224
##   0.3    0.10    0.9122533  0.6579847
##   0.4    0.00    0.9640012  0.8797477
##   0.4    0.02    0.9507512  0.8268432
##   0.4    0.04    0.9417530  0.7894936
##   0.4    0.06    0.9327521  0.7516712
##   0.4    0.08    0.9205036  0.6964730
##   0.4    0.10    0.9090027  0.6420841
##   0.5    0.00    0.9640012  0.8797477
##   0.5    0.02    0.9510018  0.8274778
##   0.5    0.04    0.9410027  0.7864150
##   0.5    0.06    0.9302505  0.7403588
##   0.5    0.08    0.9190049  0.6897157
##   0.5    0.10    0.9055027  0.6252131
##   0.6    0.00    0.9640012  0.8797477
##   0.6    0.02    0.9505021  0.8256678
##   0.6    0.04    0.9410024  0.7865206
##   0.6    0.06    0.9287508  0.7334507
##   0.6    0.08    0.9172540  0.6811039
##   0.6    0.10    0.9040027  0.6174021
##   0.7    0.00    0.9642515  0.8806617
##   0.7    0.02    0.9500015  0.8238559
##   0.7    0.04    0.9402527  0.7832490
##   0.7    0.06    0.9270027  0.7255441
##   0.7    0.08    0.9157527  0.6741029
##   0.7    0.10    0.9040018  0.6168488
##   0.8    0.00    0.9642515  0.8806617
##   0.8    0.02    0.9510002  0.8275649
##   0.8    0.04    0.9400027  0.7822849
##   0.8    0.06    0.9242524  0.7135114
##   0.8    0.08    0.9155024  0.6730202
##   0.8    0.10    0.9047524  0.6210541
##   0.9    0.00    0.9642515  0.8806617
##   0.9    0.02    0.9502506  0.8246344
##   0.9    0.04    0.9385015  0.7756153
##   0.9    0.06    0.9245027  0.7146794
##   0.9    0.08    0.9170036  0.6803886
##   0.9    0.10    0.9055018  0.6247090
##   1.0    0.00    0.9642515  0.8806617
##   1.0    0.02    0.9505012  0.8257691
##   1.0    0.04    0.9382521  0.7751520
##   1.0    0.06    0.9250021  0.7172699
##   1.0    0.08    0.9185027  0.6872799
##   1.0    0.10    0.9062521  0.6283877
## 
## Kappa was used to select the optimal model using the largest value.
## The final values used for the model were alpha = 0.7 and lambda = 0.

# Predict using the trained logistic regression model on test data
lrPred = predict(lrFit, test_data)

# Generate confusion matrix for the predictions
cm = confusionMatrix(lrPred, test_data$class)

# Print confusion matrix
cm

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction NOT.QSO  QSO
##    NOT.QSO    4796  144
##    QSO          53 1006
##                                           
##                Accuracy : 0.9672          
##                  95% CI : (0.9623, 0.9715)
##     No Information Rate : 0.8083          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.8907          
##                                           
##  Mcnemar's Test P-Value : 1.434e-10       
##                                           
##             Sensitivity : 0.9891          
##             Specificity : 0.8748          
##          Pos Pred Value : 0.9709          
##          Neg Pred Value : 0.9500          
##              Prevalence : 0.8083          
##          Detection Rate : 0.7995          
##    Detection Prevalence : 0.8235          
##       Balanced Accuracy : 0.9319          
##                                           
##        'Positive' Class : NOT.QSO         
## 

# Extract Kappa value from the confusion matrix
kappa_value <- cm$overall["Kappa"]

# Extract Accuracy value from the confusion matrix
accuracy_value = cm$overall["Accuracy"]

We have a Kappa value of 0.8907366 and an accuracy of 0.9671612, but it is not the best performance metric because not all the errors have the same impact. It is worst to classify a quasar object as non quasar than viceversa, because we will not get enough money. We are going to use a more conservative threshold chosen manually.

threshold = 0.3
lrProb = predict(lrFit, test_data, type="prob")
lrPred = rep("NOT.QSO", nrow(test_data))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
cm = confusionMatrix(factor(lrPred), test_data$class)
cm

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction NOT.QSO  QSO
##    NOT.QSO    4741  110
##    QSO         108 1040
##                                           
##                Accuracy : 0.9637          
##                  95% CI : (0.9586, 0.9683)
##     No Information Rate : 0.8083          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.8827          
##                                           
##  Mcnemar's Test P-Value : 0.946           
##                                           
##             Sensitivity : 0.9777          
##             Specificity : 0.9043          
##          Pos Pred Value : 0.9773          
##          Neg Pred Value : 0.9059          
##              Prevalence : 0.8083          
##          Detection Rate : 0.7903          
##    Detection Prevalence : 0.8086          
##       Balanced Accuracy : 0.9410          
##                                           
##        'Positive' Class : NOT.QSO         
## 

accuracy = cm$overall["Accuracy"]

We have worst accuracy raccuracy`, but we can see the tradeoff we want between the most impactful error (decreased, as we wanted) and the other (increased as a payoff).

We are going to use the ROC to compute the optimal threshold for this model, because we do not know if the one we tried is the best one.

roc.lr=roc(test_data$class ~ lrProb[,2])

## Setting levels: control = NOT.QSO, case = QSO

## Setting direction: controls < cases

plot(roc.lr, col="red",print.thres=TRUE)
legend("bottomright", legend=c("lr"), col=c("red"), lwd=2)

auc = roc.lr$auc
auc

## Area under the curve: 0.9784

optimal <- as.numeric(coords(roc.lr, "best", ret = "threshold"))

We can see that the best threshold for our benchmark model is 0.2455102 (close to the one we chose, but not the same) and it is a really good model because the AUC is incredibly high, 0.9783625. Hence, we predict using this threshold.

threshold = optimal
lrProb = predict(lrFit, test_data, type="prob")
lrPred = rep("NOT.QSO", nrow(test_data))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
cm = confusionMatrix(factor(lrPred), test_data$class)
cm

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction NOT.QSO  QSO
##    NOT.QSO    4718  100
##    QSO         131 1050
##                                           
##                Accuracy : 0.9615          
##                  95% CI : (0.9563, 0.9662)
##     No Information Rate : 0.8083          
##     P-Value [Acc > NIR] : <2e-16          
##                                           
##                   Kappa : 0.877           
##                                           
##  Mcnemar's Test P-Value : 0.0484          
##                                           
##             Sensitivity : 0.9730          
##             Specificity : 0.9130          
##          Pos Pred Value : 0.9792          
##          Neg Pred Value : 0.8891          
##              Prevalence : 0.8083          
##          Detection Rate : 0.7865          
##    Detection Prevalence : 0.8031          
##       Balanced Accuracy : 0.9430          
##                                           
##        'Positive' Class : NOT.QSO         
## 

accuracy = cm$overall["Accuracy"]

Variable importance

For Bayes’ classifiers or logistic regression, variable importance is based on estimated coefficients. Variable importance refers to the measure of the contribution of each predictor variable in a predictive model towards explaining the variability or making accurate predictions. It helps identify which variables have the most significant impact on the model’s performance.

Understanding variable importance helps in feature selection, model interpretation, and identifying which variables have the most influence on the target variable. It can guide data preprocessing efforts and provide insights into the underlying relationships between predictors and the target variable.

lr_imp <- varImp(lrFit, scale = F)
plot(lr_imp, scales = list(y = list(cex = .95)))

redshift is very important for predicting, the most important variable.

Partial dependence plots

Partial dependence plots are a visualization technique used to understand the relationship between one predictor variable and the target variable in a predictive model. They show how the average prediction of the model changes as the value of one or more predictors varies while keeping all other predictors at fixed values.

These are the marginal effects of one variable after discounting for other variables

partial(lrFit, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

We can see what we saw in the EDA, the higher the value, the bigger the probability of being a quasar object. Moreover, when the value in the redshift variable is higher than 2, the object is always classified as quasar.

Cost-sensitive Analysis

However, our goal is not only to obtain the best predictive model, but to optimize the economic profit. We do not want to end up with less money than we need. We take a look at the two possible errors:

• Classify a quasar object as ‘NOT QSO’.
• Classify a galaxy or star as ‘QSO’.

We realize that for us, both errors do not have the same impact. The first one is more costly because we will not receive all the money that we need for our research (investigating a quasar object is more expensive in our scenario) and we will not be able to do it fully. We have the following table of profits:

Prediction/Reference	NOT WSO	QSO
NOT QSO	0.25	-1.0
QSO	-0.10	0.25

We need to set the profit table as a vector:

profit.unit <- c(0.25, -0.10, -1.0, 0.25)

A naive classifier would classify all the objects as non quasar because it is the most prevalent class (80.82%), so the profit would be:

profit = 0.25*0.81 - 1*0.19 - 0.1*0 + 0.25*0 = 0.2025 - 0.19 = 0.0125 per object.

This profit is what we want to improve by obtaining the best threshold for our model and by taking into account the economic impact.

profit.i = matrix(NA, nrow = 25, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = data.frame(alpha = lrFit$bestTune$alpha, lambda = lrFit$bestTune$lambda)

seq_values <- seq(0.05, 0.45, 0.05)

# Append 0.22 to the sequence
seq_values <- c(seq_values, 0.246)

j <- 0
for (threshold in seq_values){
  
  j <- j + 1
  #cat(j)
  for(i in 1:25){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    lrFit <- train(class ~ ., data=train, method = "glmnet",
                   tuneGrid = grid, preProcess = c("center", "scale"),
                   trControl = trainControl(method = "none", classProbs = TRUE))
    
    lrProb = predict(lrFit, test, type="prob")
    lrPred = rep("NOT.QSO", nrow(test))
    lrPred[which(lrProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(lrPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
    
  }
}

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq(0.05,0.5,0.05),col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] 0.2029179 0.2156524 0.2190079 0.2195915 0.2193831 0.2195081 0.2182784
##  [8] 0.2184452 0.2157774 0.2203835

We observe that the thresholds around 0.25 are good, so we obtain the best one and compute our final prediction with this model and obtain its economic profit.

indexthr = which.max(medians)
threshold = seq(0.05,0.5,0.05)[indexthr]
lrFit <- train(class ~ ., data=train_data, method = "glmnet",
               tuneGrid = data.frame(alpha = 0.7, lambda = 0), preProcess = c("center", "scale"),
               trControl = trainControl(method = "none", classProbs = TRUE))
lrProb = predict(lrFit, test_data, type="prob")
lrPred = rep("NOT.QSO", nrow(test_data))
lrPred[which(lrProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(lrPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.2169028

We can see that we have improved the model immensely, our economic profit is now 0.2169028. However, this is just our benchmark model, the best one yet, but the benchmark nevertheless. We are going to use the machine learning tools we saw in class to try to improve it.

optimal <- as.numeric(coords(roc.lr, “best”, ret = “threshold”)) ### K-Nearest Neighbours (KNN)

K-Nearest Neighbors (KNN) is a simple and intuitive supervised learning algorithm used for classification and regression tasks. It’s a non-parametric method, meaning it doesn’t make any assumptions about the underlying data distribution. Instead, it makes predictions based on the similarity of new data points to existing data points.

KNN’s simplicity and effectiveness make it a popular choice for various machine learning tasks, especially when the dataset is small or the relationships between features and the target variable are non-linear. However, its main drawback is its computational inefficiency, particularly with large datasets, as it requires computing distances between the new data point and all training data points during prediction. Additionally, KNN’s performance can degrade if the feature space has irrelevant or noisy features, or if the data is imbalanced.

As we saw in class, using the package caret is the best choice, so we decide to compute all the models using this package. We are going to define a function that will work as our metric in our caret models, it takes into account the economic nature of our problem.

# We have to add lev = NULL and model = NULL to make caret work 
EconomicProfit <- function(data, lev = NULL, model = NULL) 
{
  y.pred = data$pred 
  y.true = data$obs
  CM = confusionMatrix(y.pred, y.true)$table
  out = sum(profit.unit*CM)/sum(CM) # The profit expression
  names(out) <- c("EconomicProfit")
  out # Important to use this name so caret think it is like accuracy
}

We also need the control function, which performs cross-validation with 5 folds.

ctrl <- trainControl(method = "cv", number = 5,
                     classProbs = TRUE, 
                     summaryFunction = EconomicProfit,
                     verbose=F)

Now, we have to train our model.

train_data$class <- factor(train_data$class, labels = make.names(levels(train_data$class)))

knnFit <- train(class ~ ., 
                method = "knn", 
                data = train_data,
                preProcess = c("center", "scale"),
                tuneLength = 7,
                metric = "EconomicProfit",
                trControl = ctrl)

## + Fold1: k= 5 
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## Aggregating results
## Selecting tuning parameters
## Fitting k = 5 on full training set

print(knnFit)

## k-Nearest Neighbors 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3200, 3199, 3201, 3200, 3200 
## Resampling results across tuning parameters:
## 
##   k   EconomicProfit
##    5  0.1554340     
##    7  0.1471215     
##    9  0.1408635     
##   11  0.1398742     
##   13  0.1341983     
##   15  0.1319976     
##   17  0.1248487     
## 
## EconomicProfit was used to select the optimal model using the largest value.
## The final value used for the model was k = 5.

best_hyperparameters <- knnFit$bestTune
profit <-  knnFit$results[knnFit$results$k == best_hyperparameters$k, "EconomicProfit"]

We see that of the 7 studied k parameters (5, 7, 9, 11, 13, 15 and 17) the one that maximizes the profit is k = 5, which is chosen by being the one with the highest economic profit (0.155434).

After training the model, we need to obtain its predictions.

knnPred = predict(knnFit, test_data)

cm = confusionMatrix(knnPred,test_data$class)
cm

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction NOT.QSO  QSO
##    NOT.QSO    4780  362
##    QSO          69  788
##                                           
##                Accuracy : 0.9282          
##                  95% CI : (0.9213, 0.9346)
##     No Information Rate : 0.8083          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.7432          
##                                           
##  Mcnemar's Test P-Value : < 2.2e-16       
##                                           
##             Sensitivity : 0.9858          
##             Specificity : 0.6852          
##          Pos Pred Value : 0.9296          
##          Neg Pred Value : 0.9195          
##              Prevalence : 0.8083          
##          Detection Rate : 0.7968          
##    Detection Prevalence : 0.8571          
##       Balanced Accuracy : 0.8355          
##                                           
##        'Positive' Class : NOT.QSO         
## 

accuracy = cm$overall["Accuracy"]

profit = EconomicProfit(data = data.frame(pred  = knnPred, obs = test_data$class))
profit

## EconomicProfit 
##      0.1705451

We obtain a pretty good model, with accuracy 0.9281547 and economic profit 0.1705451. We saw that another way of using knn is making it obtain the predictions based on the probabilities computed using the proportion of votes in the neighbours.

knnProb = predict(knnFit, test_data, type="prob") #prob to get the probabilities instead of the class predictions
head(knnProb) #We get the good and the bad probability (we only need one)

##   NOT.QSO QSO
## 1     1.0 0.0
## 2     0.8 0.2
## 3     0.2 0.8
## 4     1.0 0.0
## 5     0.6 0.4
## 6     0.8 0.2

We want to check if the model is better if we change the thresghold manually. Previously, we classified an object as QSO when the probability of being a quasar object was greater than 0.5. Now we are going to classify it as QSO when the probability is greater than 0.2 using the knn model with probabilities we just computed (more conservative because we want to reduce the number of quasar object classified as non quasar).

threshold = 0.2 
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.1858476

We have in fact increased slightly the economic profit to 0.1858476, we have a bit better profit. However, we just tried another threshold and it improved, but how do we know if it is the best one? We are going to compute the ROC curve in order to compute this optimal threshold.

roc.knn=roc(test_data$class ~ knnProb[,2])

## Setting levels: control = NOT.QSO, case = QSO

## Setting direction: controls < cases

plot(roc.knn, col="red",print.thres=TRUE, ylim = c(0, 1), xlim = c(1, 0))

optimal <- as.numeric(coords(roc.knn, "best", ret = "threshold"))

From the plot we can see that the best threshold is actually 0.2666667, so with our guess we were closer but not quite there. It is the one with the best balance between sensitivity and specificity. Now, an object is classified as a quasar object if the probability of being QSO is greater than 0.3. Being 0.3 our best threshold instead of 0.5 shows that our classes are unbalanced.

auc = roc.knn$auc
auc

## Area under the curve: 0.9202

We are sure this is a good choice because the are under the curve, as we just computed, is incredibly high 0.9201977, being the maximum 1 and minimum 0 (the higher the better).

knnProb = predict(knnFit, test_data, type="prob") 
threshold = optimal
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.1858476

However, we can observe that both threshold behave very similarly and we obtain a very similar economic profit, so both thresholds are good choices.

We have fitted a knn model that predicts with class, then we have improved it with one that predicts with probalities. Afterwards, we tried a more conservative threshold which improved it and then using the ROC curve we obtained the optimal threshold that performed as our manually chosen conservative one. When we say improved, we are talking in terms of decreasing the economic cost, not increasing the accuracy (we actually make it worse, we make more errors, but less economically costly). But we still do not know if our manually chosen threshold is the best one, we will compute the best one.

profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = knnFit$bestTune

seq = c(seq(0.05,0.45,0.05), optimal)

j <- 0
for (threshold in seq){
  
  j <- j + 1
  #cat(j)
  for(i in 1:15){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    knnFit <- train(class ~ ., 
                method = "knn", 
                data = train,
                preProcess = c("center", "scale"),
                tuneLength = 0, # k = 7
                tuneGrid = grid,
                metric = "EconomicProfit",
                trControl = ctrl)
    
    knnProb = predict(knnFit, test, type="prob")
    knnPred = rep("NOT.QSO", nrow(test))
    knnPred[which(knnProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(knnPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
  }
}

## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
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## - Fold4: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## + Fold3: k=5 
## - Fold3: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## + Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
## - Fold3: k=5 
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## - Fold4: k=5 
## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
## - Fold3: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
## - Fold2: k=5 
## + Fold3: k=5 
## - Fold3: k=5 
## + Fold4: k=5 
## - Fold4: k=5 
## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
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## + Fold3: k=5 
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## - Fold4: k=5 
## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
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## + Fold3: k=5 
## - Fold3: k=5 
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## - Fold4: k=5 
## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## + Fold5: k=5 
## - Fold5: k=5 
## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## - Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
## + Fold2: k=5 
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## + Fold3: k=5 
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## + Fold5: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
## - Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set
## + Fold1: k=5 
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## Aggregating results
## Fitting final model on full training set

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq, col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] 0.1476240 0.1491246 0.1477699 0.1643602 0.1637140 0.1655065 0.1650688
##  [8] 0.1374531 0.1372447 0.1640892

We observe that the optimal threshold for our model is around 0.3, so we compute the optimal one and make our final prediction using it.

knnProb = predict(knnFit, test_data, type="prob") 
indexthr = which.max(medians)
threshold = seq[indexthr]
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(knnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.1652859

We are not satisfied with this model, we want to see if we can improve it more. Hence, we try with Support Vector Machines (SVM) models.

Variable importance and partial dependencies

We study the variable importance to be able to plot it later.

knn_imp <- varImp(knnFit, scale = F)
plot(knn_imp, scales = list(y = list(cex = .95)))

redshift is once again the most important one.

partial(knnFit, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(knnFit, pred.var = "z", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(knnFit, pred.var = "i", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

We can see that the bigger the value in either of these 3 variables, the bigger the chance to be a quasar object.

Support Vector Machines (SVM)

Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for classification and regression tasks. It’s particularly effective in high-dimensional spaces and when the number of features is greater than the number of samples. SVM works by finding the optimal hyperplane that best separates the data points into different classes or predicts continuous outcomes.

SVMs is effective in high-dimensional spaces, versatile because it supports different kernel functions for handling non-linear relationships. and robust against overfitting, especially with proper regularization. However, SVMs can be sensitive to the choice of the kernel function and its parameters. Additionally, they can be computationally expensive, especially with large datasets, and may require careful tuning of hyperparameters for optimal performance

We are going to use caret once again, since we already defined the economic cost (what we want to maximize, our performance metric) and the control function (how we compute the hyper-parameters), we just have to use the train function with the corresponding method and arguments to train our SVM model.

svmFit <- train(class ~., method = "svmRadial", 
                data = train_data,
                preProcess = c("center", "scale"),
                tuneGrid = expand.grid(C = c(0.1, 0.25, 0.5, 0.75, 1), # grid for C
                                      sigma = c(0.01, 0.02, 0.05, 0.07, 0.1)), # grid for sigma
                metric = "EconomicProfit", # maximizing the profit again
                trControl = ctrl)

## + Fold1: C=0.10, sigma=0.01 
## - Fold1: C=0.10, sigma=0.01 
## + Fold1: C=0.25, sigma=0.01 
## - Fold1: C=0.25, sigma=0.01 
## + Fold1: C=0.50, sigma=0.01 
## - Fold1: C=0.50, sigma=0.01 
## + Fold1: C=0.75, sigma=0.01 
## - Fold1: C=0.75, sigma=0.01 
## + Fold1: C=1.00, sigma=0.01 
## - Fold1: C=1.00, sigma=0.01 
## + Fold1: C=0.10, sigma=0.02 
## - Fold1: C=0.10, sigma=0.02 
## + Fold1: C=0.25, sigma=0.02 
## - Fold1: C=0.25, sigma=0.02 
## + Fold1: C=0.50, sigma=0.02 
## - Fold1: C=0.50, sigma=0.02 
## + Fold1: C=0.75, sigma=0.02 
## - Fold1: C=0.75, sigma=0.02 
## + Fold1: C=1.00, sigma=0.02 
## - Fold1: C=1.00, sigma=0.02 
## + Fold1: C=0.10, sigma=0.05 
## - Fold1: C=0.10, sigma=0.05 
## + Fold1: C=0.25, sigma=0.05 
## - Fold1: C=0.25, sigma=0.05 
## + Fold1: C=0.50, sigma=0.05 
## - Fold1: C=0.50, sigma=0.05 
## + Fold1: C=0.75, sigma=0.05 
## - Fold1: C=0.75, sigma=0.05 
## + Fold1: C=1.00, sigma=0.05 
## - Fold1: C=1.00, sigma=0.05 
## + Fold1: C=0.10, sigma=0.07 
## - Fold1: C=0.10, sigma=0.07 
## + Fold1: C=0.25, sigma=0.07 
## - Fold1: C=0.25, sigma=0.07 
## + Fold1: C=0.50, sigma=0.07 
## - Fold1: C=0.50, sigma=0.07 
## + Fold1: C=0.75, sigma=0.07 
## - Fold1: C=0.75, sigma=0.07 
## + Fold1: C=1.00, sigma=0.07 
## - Fold1: C=1.00, sigma=0.07 
## + Fold1: C=0.10, sigma=0.10 
## - Fold1: C=0.10, sigma=0.10 
## + Fold1: C=0.25, sigma=0.10 
## - Fold1: C=0.25, sigma=0.10 
## + Fold1: C=0.50, sigma=0.10 
## - Fold1: C=0.50, sigma=0.10 
## + Fold1: C=0.75, sigma=0.10 
## - Fold1: C=0.75, sigma=0.10 
## + Fold1: C=1.00, sigma=0.10 
## - Fold1: C=1.00, sigma=0.10 
## + Fold2: C=0.10, sigma=0.01 
## - Fold2: C=0.10, sigma=0.01 
## + Fold2: C=0.25, sigma=0.01 
## - Fold2: C=0.25, sigma=0.01 
## + Fold2: C=0.50, sigma=0.01 
## - Fold2: C=0.50, sigma=0.01 
## + Fold2: C=0.75, sigma=0.01 
## - Fold2: C=0.75, sigma=0.01 
## + Fold2: C=1.00, sigma=0.01 
## - Fold2: C=1.00, sigma=0.01 
## + Fold2: C=0.10, sigma=0.02 
## - Fold2: C=0.10, sigma=0.02 
## + Fold2: C=0.25, sigma=0.02 
## - Fold2: C=0.25, sigma=0.02 
## + Fold2: C=0.50, sigma=0.02 
## - Fold2: C=0.50, sigma=0.02 
## + Fold2: C=0.75, sigma=0.02 
## - Fold2: C=0.75, sigma=0.02 
## + Fold2: C=1.00, sigma=0.02 
## - Fold2: C=1.00, sigma=0.02 
## + Fold2: C=0.10, sigma=0.05 
## - Fold2: C=0.10, sigma=0.05 
## + Fold2: C=0.25, sigma=0.05 
## - Fold2: C=0.25, sigma=0.05 
## + Fold2: C=0.50, sigma=0.05 
## - Fold2: C=0.50, sigma=0.05 
## + Fold2: C=0.75, sigma=0.05 
## - Fold2: C=0.75, sigma=0.05 
## + Fold2: C=1.00, sigma=0.05 
## - Fold2: C=1.00, sigma=0.05 
## + Fold2: C=0.10, sigma=0.07 
## - Fold2: C=0.10, sigma=0.07 
## + Fold2: C=0.25, sigma=0.07 
## - Fold2: C=0.25, sigma=0.07 
## + Fold2: C=0.50, sigma=0.07 
## - Fold2: C=0.50, sigma=0.07 
## + Fold2: C=0.75, sigma=0.07 
## - Fold2: C=0.75, sigma=0.07 
## + Fold2: C=1.00, sigma=0.07 
## - Fold2: C=1.00, sigma=0.07 
## + Fold2: C=0.10, sigma=0.10 
## - Fold2: C=0.10, sigma=0.10 
## + Fold2: C=0.25, sigma=0.10 
## - Fold2: C=0.25, sigma=0.10 
## + Fold2: C=0.50, sigma=0.10 
## - Fold2: C=0.50, sigma=0.10 
## + Fold2: C=0.75, sigma=0.10 
## - Fold2: C=0.75, sigma=0.10 
## + Fold2: C=1.00, sigma=0.10 
## - Fold2: C=1.00, sigma=0.10 
## + Fold3: C=0.10, sigma=0.01 
## - Fold3: C=0.10, sigma=0.01 
## + Fold3: C=0.25, sigma=0.01 
## - Fold3: C=0.25, sigma=0.01 
## + Fold3: C=0.50, sigma=0.01 
## - Fold3: C=0.50, sigma=0.01 
## + Fold3: C=0.75, sigma=0.01 
## - Fold3: C=0.75, sigma=0.01 
## + Fold3: C=1.00, sigma=0.01 
## - Fold3: C=1.00, sigma=0.01 
## + Fold3: C=0.10, sigma=0.02 
## - Fold3: C=0.10, sigma=0.02 
## + Fold3: C=0.25, sigma=0.02 
## - Fold3: C=0.25, sigma=0.02 
## + Fold3: C=0.50, sigma=0.02 
## - Fold3: C=0.50, sigma=0.02 
## + Fold3: C=0.75, sigma=0.02 
## - Fold3: C=0.75, sigma=0.02 
## + Fold3: C=1.00, sigma=0.02 
## - Fold3: C=1.00, sigma=0.02 
## + Fold3: C=0.10, sigma=0.05 
## - Fold3: C=0.10, sigma=0.05 
## + Fold3: C=0.25, sigma=0.05 
## - Fold3: C=0.25, sigma=0.05 
## + Fold3: C=0.50, sigma=0.05 
## - Fold3: C=0.50, sigma=0.05 
## + Fold3: C=0.75, sigma=0.05 
## - Fold3: C=0.75, sigma=0.05 
## + Fold3: C=1.00, sigma=0.05 
## - Fold3: C=1.00, sigma=0.05 
## + Fold3: C=0.10, sigma=0.07 
## - Fold3: C=0.10, sigma=0.07 
## + Fold3: C=0.25, sigma=0.07 
## - Fold3: C=0.25, sigma=0.07 
## + Fold3: C=0.50, sigma=0.07 
## - Fold3: C=0.50, sigma=0.07 
## + Fold3: C=0.75, sigma=0.07 
## - Fold3: C=0.75, sigma=0.07 
## + Fold3: C=1.00, sigma=0.07 
## - Fold3: C=1.00, sigma=0.07 
## + Fold3: C=0.10, sigma=0.10 
## - Fold3: C=0.10, sigma=0.10 
## + Fold3: C=0.25, sigma=0.10 
## - Fold3: C=0.25, sigma=0.10 
## + Fold3: C=0.50, sigma=0.10 
## - Fold3: C=0.50, sigma=0.10 
## + Fold3: C=0.75, sigma=0.10 
## - Fold3: C=0.75, sigma=0.10 
## + Fold3: C=1.00, sigma=0.10 
## - Fold3: C=1.00, sigma=0.10 
## + Fold4: C=0.10, sigma=0.01 
## - Fold4: C=0.10, sigma=0.01 
## + Fold4: C=0.25, sigma=0.01 
## - Fold4: C=0.25, sigma=0.01 
## + Fold4: C=0.50, sigma=0.01 
## - Fold4: C=0.50, sigma=0.01 
## + Fold4: C=0.75, sigma=0.01 
## - Fold4: C=0.75, sigma=0.01 
## + Fold4: C=1.00, sigma=0.01 
## - Fold4: C=1.00, sigma=0.01 
## + Fold4: C=0.10, sigma=0.02 
## - Fold4: C=0.10, sigma=0.02 
## + Fold4: C=0.25, sigma=0.02 
## - Fold4: C=0.25, sigma=0.02 
## + Fold4: C=0.50, sigma=0.02 
## - Fold4: C=0.50, sigma=0.02 
## + Fold4: C=0.75, sigma=0.02 
## - Fold4: C=0.75, sigma=0.02 
## + Fold4: C=1.00, sigma=0.02 
## - Fold4: C=1.00, sigma=0.02 
## + Fold4: C=0.10, sigma=0.05 
## - Fold4: C=0.10, sigma=0.05 
## + Fold4: C=0.25, sigma=0.05 
## - Fold4: C=0.25, sigma=0.05 
## + Fold4: C=0.50, sigma=0.05 
## - Fold4: C=0.50, sigma=0.05 
## + Fold4: C=0.75, sigma=0.05 
## - Fold4: C=0.75, sigma=0.05 
## + Fold4: C=1.00, sigma=0.05 
## - Fold4: C=1.00, sigma=0.05 
## + Fold4: C=0.10, sigma=0.07 
## - Fold4: C=0.10, sigma=0.07 
## + Fold4: C=0.25, sigma=0.07 
## - Fold4: C=0.25, sigma=0.07 
## + Fold4: C=0.50, sigma=0.07 
## - Fold4: C=0.50, sigma=0.07 
## + Fold4: C=0.75, sigma=0.07 
## - Fold4: C=0.75, sigma=0.07 
## + Fold4: C=1.00, sigma=0.07 
## - Fold4: C=1.00, sigma=0.07 
## + Fold4: C=0.10, sigma=0.10 
## - Fold4: C=0.10, sigma=0.10 
## + Fold4: C=0.25, sigma=0.10 
## - Fold4: C=0.25, sigma=0.10 
## + Fold4: C=0.50, sigma=0.10 
## - Fold4: C=0.50, sigma=0.10 
## + Fold4: C=0.75, sigma=0.10 
## - Fold4: C=0.75, sigma=0.10 
## + Fold4: C=1.00, sigma=0.10 
## - Fold4: C=1.00, sigma=0.10 
## + Fold5: C=0.10, sigma=0.01 
## - Fold5: C=0.10, sigma=0.01 
## + Fold5: C=0.25, sigma=0.01 
## - Fold5: C=0.25, sigma=0.01 
## + Fold5: C=0.50, sigma=0.01 
## - Fold5: C=0.50, sigma=0.01 
## + Fold5: C=0.75, sigma=0.01 
## - Fold5: C=0.75, sigma=0.01 
## + Fold5: C=1.00, sigma=0.01 
## - Fold5: C=1.00, sigma=0.01 
## + Fold5: C=0.10, sigma=0.02 
## - Fold5: C=0.10, sigma=0.02 
## + Fold5: C=0.25, sigma=0.02 
## - Fold5: C=0.25, sigma=0.02 
## + Fold5: C=0.50, sigma=0.02 
## - Fold5: C=0.50, sigma=0.02 
## + Fold5: C=0.75, sigma=0.02 
## - Fold5: C=0.75, sigma=0.02 
## + Fold5: C=1.00, sigma=0.02 
## - Fold5: C=1.00, sigma=0.02 
## + Fold5: C=0.10, sigma=0.05 
## - Fold5: C=0.10, sigma=0.05 
## + Fold5: C=0.25, sigma=0.05 
## - Fold5: C=0.25, sigma=0.05 
## + Fold5: C=0.50, sigma=0.05 
## - Fold5: C=0.50, sigma=0.05 
## + Fold5: C=0.75, sigma=0.05 
## - Fold5: C=0.75, sigma=0.05 
## + Fold5: C=1.00, sigma=0.05 
## - Fold5: C=1.00, sigma=0.05 
## + Fold5: C=0.10, sigma=0.07 
## - Fold5: C=0.10, sigma=0.07 
## + Fold5: C=0.25, sigma=0.07 
## - Fold5: C=0.25, sigma=0.07 
## + Fold5: C=0.50, sigma=0.07 
## - Fold5: C=0.50, sigma=0.07 
## + Fold5: C=0.75, sigma=0.07 
## - Fold5: C=0.75, sigma=0.07 
## + Fold5: C=1.00, sigma=0.07 
## - Fold5: C=1.00, sigma=0.07 
## + Fold5: C=0.10, sigma=0.10 
## - Fold5: C=0.10, sigma=0.10 
## + Fold5: C=0.25, sigma=0.10 
## - Fold5: C=0.25, sigma=0.10 
## + Fold5: C=0.50, sigma=0.10 
## - Fold5: C=0.50, sigma=0.10 
## + Fold5: C=0.75, sigma=0.10 
## - Fold5: C=0.75, sigma=0.10 
## + Fold5: C=1.00, sigma=0.10 
## - Fold5: C=1.00, sigma=0.10 
## Aggregating results
## Selecting tuning parameters
## Fitting sigma = 0.02, C = 0.75 on full training set

print(svmFit)

## Support Vector Machines with Radial Basis Function Kernel 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3200, 3199, 3201, 3200, 3200 
## Resampling results across tuning parameters:
## 
##   C     sigma  EconomicProfit
##   0.10  0.01   0.2185235     
##   0.10  0.02   0.2189612     
##   0.10  0.05   0.2176730     
##   0.10  0.07   0.2161739     
##   0.10  0.10   0.2143724     
##   0.25  0.01   0.2193991     
##   0.25  0.02   0.2196109     
##   0.25  0.05   0.2196602     
##   0.25  0.07   0.2189609     
##   0.25  0.10   0.2128101     
##   0.50  0.01   0.2204616     
##   0.50  0.02   0.2203235     
##   0.50  0.05   0.2211235     
##   0.50  0.07   0.2196605     
##   0.50  0.10   0.2159617     
##   0.75  0.01   0.2204986     
##   0.75  0.02   0.2212114     
##   0.75  0.05   0.2197360     
##   0.75  0.07   0.2192986     
##   0.75  0.10   0.2166739     
##   1.00  0.01   0.2208116     
##   1.00  0.02   0.2205365     
##   1.00  0.05   0.2199606     
##   1.00  0.07   0.2193861     
##   1.00  0.10   0.2154602     
## 
## EconomicProfit was used to select the optimal model using the largest value.
## The final values used for the model were sigma = 0.02 and C = 0.75.

best_hyperparameters <- svmFit$bestTune
best_row <- svmFit$results[svmFit$results$sigma == best_hyperparameters$sigma & svmFit$results$C == best_hyperparameters$C, ]
profit <- best_row$EconomicProfit

If we look at the economic cost associated with every combination of possible values for our hyper-parameters, we see that the best combination is sigma = 0.02 and c = 0.75 with economic profit 0.2212114. We store the hyperparameters.

svmhyp = best_hyperparameters

After training it and doing hyper-parameter tuning, we obtain the predictions of our test dataset.

svmPred = predict(svmFit, test_data)
head(svmPred)

## [1] NOT.QSO NOT.QSO QSO     NOT.QSO NOT.QSO NOT.QSO
## Levels: NOT.QSO QSO

cm = confusionMatrix(svmPred,test_data$class)
cm

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction NOT.QSO  QSO
##    NOT.QSO    4811  122
##    QSO          38 1028
##                                           
##                Accuracy : 0.9733          
##                  95% CI : (0.9689, 0.9773)
##     No Information Rate : 0.8083          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.9115          
##                                           
##  Mcnemar's Test P-Value : 5.319e-11       
##                                           
##             Sensitivity : 0.9922          
##             Specificity : 0.8939          
##          Pos Pred Value : 0.9753          
##          Neg Pred Value : 0.9644          
##              Prevalence : 0.8083          
##          Detection Rate : 0.8020          
##    Detection Prevalence : 0.8223          
##       Balanced Accuracy : 0.9430          
##                                           
##        'Positive' Class : NOT.QSO         
## 

accuracy = cm$overall["Accuracy"]
profit = EconomicProfit(data = data.frame(pred  = svmPred, obs = test_data$class))
profit

## EconomicProfit 
##      0.2223621

The performance is really good, the accuracy is 0.9733289 , but, more importantly, the economic profit is 0.2223621, which is the best profit yet, better than all the knn ones we computed and the benchmark too.

As we did in knn, we are going to take another approach and use the probabilities from SVM, which in this case are calibrated using Platt scaling (logistic regression on the SVM’s scores).

svmProb = predict(svmFit, test_data, type="prob")
head(svmProb)

##        NOT.QSO        QSO
## 1 9.867154e-01 0.01328455
## 2 9.417532e-01 0.05824677
## 3 8.920199e-07 0.99999911
## 4 9.792094e-01 0.02079062
## 5 9.716801e-01 0.02831986
## 6 9.549413e-01 0.04505865

We are going to use this model to compute the predictions changing thresholds. As we did in knn, first we try with the manually chosen 0.2 threshold.

threshold = 0.3
Cred.pred = rep("NOT.QSO", nrow(test_data)) # All good's
Cred.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad

CM = confusionMatrix(factor(Cred.pred), test_data$class)
accuracy = CM$overall["Accuracy"]
profit <- sum(profit.unit*CM$table)/sum(CM$table)
profit

## [1] 0.2240873

It is slightly better again, with an economic profit of 0.2240873 and accuracy of 0.9688281. Once again, we want to look for the optimal threshold, so we use again the ROC.

roc.svm=roc(test_data$class ~ svmProb[,2])

## Setting levels: control = NOT.QSO, case = QSO

## Setting direction: controls < cases

plot(roc.knn, col="red",print.thres=TRUE, ylim = c(0, 1), xlim = c(1, 0)) # ROC curve for knn
plot(roc.svm, add=TRUE, col='blue',print.thres=TRUE) # ROC curve for SVM
legend("bottomright", legend=c("knn", "svm"), col=c("red", "blue"), lwd=2)

optimal <- as.numeric(coords(roc.svm, "best", ret = "threshold"))

Here, we can see that the optimal threshold for SVM with probabilities is 0.2596884. Moreover, and more importantly, since we have plotted both models (knn and svm), we can see that svm is better (we have also seen it in the increase in economic profit) because the area under the curve is bigger, as we can check.

auc = roc.svm$auc
auc

## Area under the curve: 0.9772

Incredibly high, even higher than the knn one, our AUC is now 0.9772102.

So our better model yet is an svm model according to the ROC is the one with threshold 0.2596884. We want to check how much the economic profit improves (we had economic profit = 0.2240873 with threshold 0.2).

threshold = optimal
Cred.pred = rep("NOT.QSO", nrow(test_data)) # All good's
Cred.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad

CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2247625

In this case, we can see that this ‘optimal’ threshold is actually more or less the same as the 0.2 we tried, but very slightly worse.

We have to keep in mind that the ROC does not take into account the economic aspect of our task, it only takes into account the specificity and the sensitivity. This is why the ‘optimal’ threshold we obtain from it is worse than our chosen one. This is why, as in knn, we are going to compute the optimal threshold taking into account the economic aspect.

profit.i = matrix(NA, nrow = 5, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = best_hyperparameters

seq_values <- seq(0.05, 0.45, 0.05)

# Append 0.22 to the sequence
seq_values <- c(seq_values, optimal)

j <- 0
for (threshold in seq_values){
  
  j <- j + 1
  #cat(j)
  for(i in 1:5){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    svmFit <- train(class ~., method = "svmRadial", 
                data = train,
                preProcess = c("center", "scale"),
                tuneGrid = grid, 
                metric = "EconomicProfit", # maximizing the profit again
                trControl = ctrl)
  
    
    svmProb = predict(svmFit, test, type="prob")
    svmPred = rep("NOT.QSO", nrow(test))
    svmPred[which(svmProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(svmPred),test$class)$table
    profit = sum(profit.unit*CM)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
    
  }
}

## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set
## + Fold1: sigma=0.02, C=0.75 
## - Fold1: sigma=0.02, C=0.75 
## + Fold2: sigma=0.02, C=0.75 
## - Fold2: sigma=0.02, C=0.75 
## + Fold3: sigma=0.02, C=0.75 
## - Fold3: sigma=0.02, C=0.75 
## + Fold4: sigma=0.02, C=0.75 
## - Fold4: sigma=0.02, C=0.75 
## + Fold5: sigma=0.02, C=0.75 
## - Fold5: sigma=0.02, C=0.75 
## Aggregating results
## Fitting final model on full training set

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq_values,col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] 0.2097957 0.2213631 0.2239058 0.2238224 0.2221342 0.2221967 0.2242393
##  [8] 0.2208212 0.2196749 0.2207795

We compute the optimal threshold and then, we make the final prediction for this model.

svmProb = predict(svmFit, test_data, type="prob")
indexthr = which.max(medians)
threshold = seq_values[indexthr]
Cred.pred = rep("NOT.QSO", nrow(test_data)) # All good's
Cred.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad

CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2234289

We have now an economic profit of 0.2234289. We see that it performs worse than with the one of the ROC, but his may be because it actually is not better but by chance in this exact chance it is. However, doing cross-validation in the loop we see that the one that performs better on the average is 0.35.

We store its profit and threshold.

svmprofit = profit
svmoptimal = threshold

Variable importance and partial dependencies

We study which variables are the most influencial in our prediction.

svm_imp <- varImp(svmFit, scale = F)
plot(svm_imp, scales = list(y = list(cex = .95)))

Once again, redshift is the most influential one followed by zand i.

partial(svmFit, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(svmFit, pred.var = "z", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(svmFit, pred.var = "i", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

We can see what we saw in the EDA and on the penalized logistic regression model, the higher the value, the bigger the probability of being a quasar object. Moreover, when the value in the redshift variable is higher than 2, the object is always classified as quasar. Moreover, we can see that both variables zand i have the same dependency with `class. The higher their value, the higher the probability that the object is classified as quasar object.

Decision trees

Decision Trees are versatile and powerful supervised machine learning algorithms used for both classification and regression tasks. They create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

Decision trees are easy to interpret and visualize, they have the ability to handle both numerical and categorical data without the need for feature scaling and they are robustness to outliers. However, decision trees can suffer from overfitting, particularly when the tree grows too deep and captures noise in the training data. Ensemble methods like Random Forests and Gradient Boosting Machines are often used to mitigate this issue by combining multiple decision trees to improve predictive performance, we will implement them later on.

We are going to use the function available in the caret package again. We first fit the model with our economic profit condition.

grid_c50 <- expand.grid( .winnow = c(TRUE,FALSE), .trials=c(1,5,10,15,20), .model="tree" )

fit.c50 <- train(class ~.,
                data=train_data,
                method="C5.0",
                metric="EconomicProfit",
                tuneGrid = grid_c50,
                trControl = ctrl)

## + Fold1: model=tree, winnow=FALSE, trials=20 
## - Fold1: model=tree, winnow=FALSE, trials=20 
## + Fold1: model=tree, winnow= TRUE, trials=20 
## - Fold1: model=tree, winnow= TRUE, trials=20 
## + Fold2: model=tree, winnow=FALSE, trials=20 
## - Fold2: model=tree, winnow=FALSE, trials=20 
## + Fold2: model=tree, winnow= TRUE, trials=20 
## - Fold2: model=tree, winnow= TRUE, trials=20 
## + Fold3: model=tree, winnow=FALSE, trials=20 
## - Fold3: model=tree, winnow=FALSE, trials=20 
## + Fold3: model=tree, winnow= TRUE, trials=20 
## - Fold3: model=tree, winnow= TRUE, trials=20 
## + Fold4: model=tree, winnow=FALSE, trials=20 
## - Fold4: model=tree, winnow=FALSE, trials=20 
## + Fold4: model=tree, winnow= TRUE, trials=20 
## - Fold4: model=tree, winnow= TRUE, trials=20 
## + Fold5: model=tree, winnow=FALSE, trials=20 
## - Fold5: model=tree, winnow=FALSE, trials=20 
## + Fold5: model=tree, winnow= TRUE, trials=20 
## - Fold5: model=tree, winnow= TRUE, trials=20 
## Aggregating results
## Selecting tuning parameters
## Fitting trials = 10, model = tree, winnow = TRUE on full training set

fit.c50

## C5.0 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3200, 3200, 3200, 3200, 3200 
## Resampling results across tuning parameters:
## 
##   winnow  trials  EconomicProfit
##   FALSE    1      0.2205750     
##   FALSE    5      0.2229375     
##   FALSE   10      0.2215500     
##   FALSE   15      0.2217625     
##   FALSE   20      0.2228375     
##    TRUE    1      0.2178375     
##    TRUE    5      0.2196875     
##    TRUE   10      0.2239875     
##    TRUE   15      0.2231375     
##    TRUE   20      0.2237125     
## 
## Tuning parameter 'model' was held constant at a value of tree
## EconomicProfit was used to select the optimal model using the largest value.
## The final values used for the model were trials = 10, model = tree and winnow
##  = TRUE.

best_hyperparameters <- fit.c50$bestTune
best_row <- fit.c50$results[fit.c50$results$winnow == best_hyperparameters$winnow & 
                            fit.c50$results$trials == best_hyperparameters$trials & 
                            fit.c50$results$model == best_hyperparameters$model, ]
profit <- best_row$EconomicProfit

Once again, we look at all the combination and their resulting economic profit to choose the combination with the highest economic profit, which is 0.2239875 with winnow = TRUE and 10 trials. We store their values.

dthyp = best_hyperparameters

we are going to take a visual look at our tree with the summary function.

summary(fit.c50)

## 
## Call:
## (function (x, y, trials = 1, rules = FALSE, weights = NULL, control
##  = TRUE, noGlobalPruning = FALSE, CF = 0.25, minCases = 2, fuzzyThreshold
##  = FALSE, sample = 0, earlyStopping = TRUE, label = "outcome", seed = 592L))
## 
## 
## C5.0 [Release 2.07 GPL Edition]      Wed Mar 13 23:04:19 2024
## -------------------------------
## 
## Class specified by attribute `outcome'
## 
## Read 4000 cases (27 attributes) from undefined.data
## 
## 6 attributes winnowed
## Estimated importance of remaining attributes:
## 
##     638%  redshift
##      46%  u
##      24%  g
##      14%  spec_obj_ID
##       2%  z
##       2%  field_ID
##      <1%  alpha
##      <1%  delta
##      <1%  r
##      <1%  i
##      <1%  run_ID1000 - 2000
##      <1%  run_ID2000 - 3000
##      <1%  run_ID3000 - 4000
##      <1%  run_ID5000 - 6000
##      <1%  run_ID7000 - 8000
##      <1%  run_ID8000 - 9000
##      <1%  cam_col3
##      <1%  cam_col4
##      <1%  cam_col5
##      <1%  cam_col6
##      <1%  MJD
## 
## -----  Trial 0:  -----
## 
## Decision tree:
## 
## redshift > 0.9874022:
## :...g <= 22.3466: QSO (591/12)
## :   g > 22.3466:
## :   :...redshift > 1.726004: QSO (17)
## :       redshift <= 1.726004:
## :       :...spec_obj_ID <= 9.619727e+18: QSO (13/2)
## :           spec_obj_ID > 9.619727e+18: NOT.QSO (32/6)
## redshift <= 0.9874022:
## :...redshift <= 0.6092985:
##     :...u <= 19.74477:
##     :   :...redshift <= 0.1674231: NOT.QSO (599/1)
##     :   :   redshift > 0.1674231:
##     :   :   :...redshift <= 0.2514768: NOT.QSO (12/5)
##     :   :       redshift > 0.2514768: QSO (13)
##     :   u > 19.74477:
##     :   :...z <= 19.62704: NOT.QSO (1703/14)
##     :       z > 19.62704:
##     :       :...redshift <= 0.07216766: NOT.QSO (244)
##     :           redshift > 0.07216766:
##     :           :...g > 21.06557: NOT.QSO (139/17)
##     :               g <= 21.06557:
##     :               :...redshift <= 0.3640681: NOT.QSO (3)
##     :                   redshift > 0.3640681: QSO (7)
##     redshift > 0.6092985:
##     :...u > 21.93472: NOT.QSO (542/30)
##         u <= 21.93472:
##         :...g > 21.76308: NOT.QSO (7)
##             g <= 21.76308:
##             :...run_ID7000 - 8000 <= 0: QSO (62/5)
##                 run_ID7000 - 8000 > 0:
##                 :...z <= 19.47418: NOT.QSO (4)
##                     z > 19.47418:
##                     :...r <= 21.20465: QSO (10)
##                         r > 21.20465: NOT.QSO (2)
## 
## -----  Trial 1:  -----
## 
## Decision tree:
## 
## redshift <= 0.786133:
## :...i > 20.97315:
## :   :...redshift <= 0.07216766: NOT.QSO (71.8)
## :   :   redshift > 0.07216766: QSO (237.8/65.8)
## :   i <= 20.97315:
## :   :...redshift <= 0.1674231: NOT.QSO (1037.9/11.4)
## :       redshift > 0.1674231:
## :       :...u <= 21.04776: QSO (223.4/52.9)
## :           u > 21.04776:
## :           :...i <= 18.86458: NOT.QSO (300.1)
## :               i > 18.86458:
## :               :...redshift <= 0.2363302: QSO (40.2/6)
## :                   redshift > 0.2363302:
## :                   :...u > 22.80355: NOT.QSO (641.9/45.5)
## :                       u <= 22.80355:
## :                       :...z <= 19.43995: NOT.QSO (181.6/24.3)
## :                           z > 19.43995: QSO (161.3/46.1)
## redshift > 0.786133:
## :...redshift > 1.638417: QSO (301.6/0.8)
##     redshift <= 1.638417:
##     :...u <= 21.56305: QSO (119.4)
##         u > 21.56305:
##         :...spec_obj_ID <= 8.147113e+18: QSO (117.4/5.3)
##             spec_obj_ID > 8.147113e+18:
##             :...spec_obj_ID > 1.209107e+19: QSO (77.3/8.3)
##                 spec_obj_ID <= 1.209107e+19:
##                 :...run_ID5000 - 6000 > 0: NOT.QSO (50.7)
##                     run_ID5000 - 6000 <= 0:
##                     :...run_ID7000 - 8000 > 0: NOT.QSO (116.6/15.9)
##                         run_ID7000 - 8000 <= 0:
##                         :...u <= 25.8097: QSO (290/126.4)
##                             u > 25.8097: NOT.QSO (31.1/0.8)
## 
## -----  Trial 2:  -----
## 
## Decision tree:
## 
## redshift > 0.8952028:
## :...redshift > 1.667782: QSO (220.1)
## :   redshift <= 1.667782:
## :   :...g <= 21.14217: QSO (64.7)
## :       g > 21.14217:
## :       :...g > 24.25772: NOT.QSO (26.4/0.6)
## :           g <= 24.25772:
## :           :...MJD > 58252: QSO (31.8)
## :               MJD <= 58252:
## :               :...MJD <= 57015: QSO (118.2/35.7)
## :                   MJD > 57015: NOT.QSO (348.5/131.9)
## redshift <= 0.8952028:
## :...redshift <= 0.02193579: NOT.QSO (542.6)
##     redshift > 0.02193579:
##     :...alpha > 358.1465: QSO (49.6/13.2)
##         alpha <= 358.1465:
##         :...i <= 17.28261: NOT.QSO (431.2/9.4)
##             i > 17.28261:
##             :...u > 23.83094: NOT.QSO (587.3/40.3)
##                 u <= 23.83094:
##                 :...z > 22.34539: NOT.QSO (41.4)
##                     z <= 22.34539:
##                     :...redshift <= 0.1263195: NOT.QSO (36.7)
##                         redshift > 0.1263195:
##                         :...u <= 21.18736: QSO (218.9/85.4)
##                             u > 21.18736:
##                             :...z <= 18.468: NOT.QSO (169.7)
##                                 z > 18.468:
##                                 :...u > 23.7849: QSO (43.7/7.3)
##                                     u <= 23.7849:
##                                     :...spec_obj_ID > 1.228137e+19: QSO (77.7/22.9)
##                                         spec_obj_ID <= 1.228137e+19:
##                                         :...z <= 19.97678: NOT.QSO (537.4/102.1)
##                                             z > 19.97678:
##                                             :...spec_obj_ID <= 8.147113e+18: QSO (120.6/23.9)
##                                                 spec_obj_ID > 8.147113e+18: NOT.QSO (333.5/75.5)
## 
## -----  Trial 3:  -----
## 
## Decision tree:
## 
## redshift <= 0.8710588:
## :...redshift <= 0.02193579: NOT.QSO (428.5)
## :   redshift > 0.02193579:
## :   :...z <= 18.46682:
## :       :...redshift <= 0.1674231: NOT.QSO (314)
## :       :   redshift > 0.1674231:
## :       :   :...u <= 19.68214: QSO (52.5/10)
## :       :       u > 19.68214: NOT.QSO (438.9/21)
## :       z > 18.46682:
## :       :...i > 22.2247: QSO (46.4/10)
## :           i <= 22.2247:
## :           :...u > 22.87833: NOT.QSO (786.6/94.9)
## :               u <= 22.87833:
## :               :...z > 21.5214: NOT.QSO (56.9)
## :                   z <= 21.5214:
## :                   :...delta <= 25.62602:
## :                       :...i <= 21.11553: NOT.QSO (353.3/82.1)
## :                       :   i > 21.11553: QSO (113.2/40)
## :                       delta > 25.62602:
## :                       :...g > 22.62975: NOT.QSO (39.7)
## :                           g <= 22.62975:
## :                           :...delta <= 52.41642: QSO (314.7/77.7)
## :                               delta > 52.41642: NOT.QSO (56.7/14)
## redshift > 0.8710588:
## :...u <= 22.09596: QSO (391.5/19.8)
##     u > 22.09596:
##     :...redshift > 1.638417: QSO (80.6)
##         redshift <= 1.638417:
##         :...alpha > 341.0215: NOT.QSO (34.3/0.9)
##             alpha <= 341.0215:
##             :...alpha > 336.5887: QSO (28.6)
##                 alpha <= 336.5887:
##                 :...redshift > 1.556532: NOT.QSO (22.7/0.9)
##                     redshift <= 1.556532:
##                     :...MJD > 58158: QSO (70.9/8.4)
##                         MJD <= 58158:
##                         :...u <= 22.21124: QSO (17.8)
##                             u > 22.21124:
##                             :...r > 22.70459: NOT.QSO (18.6)
##                                 r <= 22.70459:
##                                 :...r <= 22.58526: NOT.QSO (304.1/115.3)
##                                     r > 22.58526: QSO (29.7)
## 
## -----  Trial 4:  -----
## 
## Decision tree:
## 
## redshift > 0.9408078:
## :...spec_obj_ID <= 9.865324e+18: QSO (528.1/50.6)
## :   spec_obj_ID > 9.865324e+18:
## :   :...MJD <= 57938: NOT.QSO (98.2/18.6)
## :       MJD > 57938: QSO (223.5/55.9)
## redshift <= 0.9408078:
## :...redshift <= 0.02193579: NOT.QSO (336.5)
##     redshift > 0.02193579:
##     :...u > 25.75616: NOT.QSO (99.9)
##         u <= 25.75616:
##         :...u > 25.61604: QSO (58.1/11.7)
##             u <= 25.61604:
##             :...r <= 17.83108: NOT.QSO (300.9/12.9)
##                 r > 17.83108:
##                 :...g > 21.64636:
##                     :...g > 24.65492: QSO (54.5/19.4)
##                     :   g <= 24.65492:
##                     :   :...z <= 19.37151: NOT.QSO (258.7)
##                     :       z > 19.37151:
##                     :       :...u > 23.78781: NOT.QSO (166.2)
##                     :           u <= 23.78781:
##                     :           :...u > 23.75353: QSO (28.3/2.7)
##                     :               u <= 23.75353:
##                     :               :...i <= 22.17304: NOT.QSO (656.2/142.4)
##                     :                   i > 22.17304: QSO (25.8/6.7)
##                     g <= 21.64636:
##                     :...redshift > 0.6401274: QSO (295.3/63)
##                         redshift <= 0.6401274:
##                         :...u > 23.8334: NOT.QSO (50.3)
##                             u <= 23.8334:
##                             :...u > 23.80195: QSO (29/3.4)
##                                 u <= 23.80195:
##                                 :...run_ID8000 - 9000 > 0: QSO (47.8/16.6)
##                                     run_ID8000 - 9000 <= 0:
##                                     :...alpha > 357.2946: QSO (38.6/9.1)
##                                         alpha <= 357.2946:
##                                         :...redshift <= 0.1703892: NOT.QSO (102.9)
##                                             redshift > 0.1703892:
##                                             :...g <= 19.24383: QSO (52/17.1)
##                                                 g > 19.24383:
##                                                 :...z <= 18.46494: NOT.QSO (152.5/5.9)
##                                                     z > 18.46494:
##                                                     :...z <= 18.47387: QSO (20.7/0.4)
##                                                         z > 18.47387: NOT.QSO (376/113.4)
## 
## -----  Trial 5:  -----
## 
## Decision tree:
## 
## redshift > 0.9408078:
## :...redshift > 1.667782: QSO (167.2)
## :   redshift <= 1.667782:
## :   :...r <= 21.80325: QSO (450.1/97.5)
## :       r > 21.80325: NOT.QSO (264/101.9)
## redshift <= 0.9408078:
## :...redshift <= 0.02193579: NOT.QSO (265.8)
##     redshift > 0.02193579:
##     :...z <= 19.45234:
##         :...u > 23.83094: NOT.QSO (238.5)
##         :   u <= 23.83094:
##         :   :...u <= 18.01646: QSO (33/10.4)
##         :       u > 18.01646:
##         :       :...i <= 17.28261: NOT.QSO (233.9)
##         :           i > 17.28261:
##         :           :...run_ID7000 - 8000 > 0: NOT.QSO (68.6)
##         :               run_ID7000 - 8000 <= 0:
##         :               :...redshift <= 0.1696456: NOT.QSO (71.3)
##         :                   redshift > 0.1696456:
##         :                   :...u <= 21.16582: QSO (212.3/89.5)
##         :                       u > 21.16582: NOT.QSO (545/92.3)
##         z > 19.45234:
##         :...g <= 21.08929: QSO (174.2/16.1)
##             g > 21.08929:
##             :...g > 22.24555:
##                 :...redshift <= 0.1810932: QSO (34.3/6.6)
##                 :   redshift > 0.1810932:
##                 :   :...delta <= -4.478744: QSO (33.9/11.1)
##                 :       delta > -4.478744: NOT.QSO (557.8/80)
##                 g <= 22.24555:
##                 :...r > 21.92936: NOT.QSO (87.4/8.6)
##                     r <= 21.92936:
##                     :...r > 21.71312: QSO (58.2/1.2)
##                         r <= 21.71312:
##                         :...alpha > 247.0963: QSO (110.9/23.7)
##                             alpha <= 247.0963:
##                             :...redshift <= 0.4894672: NOT.QSO (79)
##                                 redshift > 0.4894672:
##                                 :...redshift <= 0.538325: QSO (42.9/0.9)
##                                     redshift > 0.538325: NOT.QSO (272/103)
## 
## -----  Trial 6:  -----
## 
## Decision tree:
## 
## z <= 18.72262:
## :...redshift <= 0.1674231: NOT.QSO (431.3)
## :   redshift > 0.1674231:
## :   :...u <= 19.68214: QSO (74.8/16)
## :       u > 19.68214: NOT.QSO (530.7/43.8)
## z > 18.72262:
## :...redshift > 1.263215:
##     :...i <= 22.25686: QSO (384.8/45.9)
##     :   i > 22.25686: NOT.QSO (28.3/4.9)
##     redshift <= 1.263215:
##     :...redshift <= 0.01859772: NOT.QSO (84.2)
##         redshift > 0.01859772:
##         :...u <= 22.24435:
##             :...redshift <= 0.3422371: NOT.QSO (102.3/16.2)
##             :   redshift > 0.3422371:
##             :   :...u <= 21.54826: QSO (243.2/21.7)
##             :       u > 21.54826:
##             :       :...i <= 19.64818: NOT.QSO (29.4)
##             :           i > 19.64818:
##             :           :...g > 22.69906: NOT.QSO (21)
##             :               g <= 22.69906:
##             :               :...u <= 21.58492: NOT.QSO (29.6/2.1)
##             :                   u > 21.58492:
##             :                   :...run_ID2000 - 3000 <= 0: QSO (303.2/81.2)
##             :                       run_ID2000 - 3000 > 0: NOT.QSO (33.1/10.5)
##             u > 22.24435:
##             :...r <= 20.01771: QSO (45.6/8.3)
##                 r > 20.01771:
##                 :...run_ID1000 - 2000 > 0: NOT.QSO (57.6)
##                     run_ID1000 - 2000 <= 0:
##                     :...run_ID8000 - 9000 > 0: NOT.QSO (123/3.5)
##                         run_ID8000 - 9000 <= 0:
##                         :...delta > 50.22329: QSO (141.7/46.8)
##                             delta <= 50.22329:
##                             :...spec_obj_ID <= 4.215504e+18: QSO (48/9.5)
##                                 spec_obj_ID > 4.215504e+18:
##                                 :...i <= 20.54975: NOT.QSO (458.4/37.4)
##                                     i > 20.54975:
##                                     :...z > 22.51591: NOT.QSO (49)
##                                         z <= 22.51591:
##                                         :...spec_obj_ID <= 8.863335e+18: QSO (203.9/57.9)
##                                             spec_obj_ID > 8.863335e+18:
##                                             :...z > 22.14288: QSO (48.8/10.2)
##                                                 z <= 22.14288:
##                                                 :...delta > 40.83349: QSO (31.1/4.4)
##                                                     delta <= 40.83349: [S1]
## 
## SubTree [S1]
## 
## spec_obj_ID <= 1.283202e+19: NOT.QSO (465.4/91.1)
## spec_obj_ID > 1.283202e+19: QSO (31.6/6.6)
## 
## -----  Trial 7:  -----
## 
## Decision tree:
## 
## redshift > 0.9787375:
## :...redshift > 1.667782: QSO (145.4)
## :   redshift <= 1.667782:
## :   :...spec_obj_ID <= 8.209184e+18: QSO (70)
## :       spec_obj_ID > 8.209184e+18:
## :       :...g <= 21.35246: QSO (88.7/8.2)
## :           g > 21.35246:
## :           :...delta > 48.92759: NOT.QSO (68.4/7.6)
## :               delta <= 48.92759:
## :               :...delta <= 32.23484: NOT.QSO (288.8/120.5)
## :                   delta > 32.23484: QSO (107)
## redshift <= 0.9787375:
## :...z > 19.8615:
##     :...redshift <= 0.01859772: NOT.QSO (38.1)
##     :   redshift > 0.01859772:
##     :   :...z > 22.17026: NOT.QSO (93.6/4.7)
##     :       z <= 22.17026:
##     :       :...MJD > 58258: QSO (80.8/17.1)
##     :           MJD <= 58258:
##     :           :...alpha <= 14.67885: NOT.QSO (60.6/4.5)
##     :               alpha > 14.67885:
##     :               :...spec_obj_ID > 1.077716e+19: NOT.QSO (111.4/10.8)
##     :                   spec_obj_ID <= 1.077716e+19:
##     :                   :...MJD > 58069: NOT.QSO (33.2)
##     :                       MJD <= 58069:
##     :                       :...MJD <= 54508: NOT.QSO (29.3)
##     :                           MJD > 54508:
##     :                           :...delta > 56.74844: QSO (34.1/3.5)
##     :                               delta <= 56.74844:
##     :                               :...delta > 52.91341: NOT.QSO (39.5/2.1)
##     :                                   delta <= 52.91341:
##     :                                   :...alpha <= 19.40138: QSO (50.5/7.2)
##     :                                       alpha > 19.40138:
##     :                                       :...delta > 49.32496: QSO (52/8.1)
##     :                                           delta <= 49.32496: [S1]
##     z <= 19.8615:
##     :...u > 23.83094: NOT.QSO (427.1)
##         u <= 23.83094:
##         :...redshift <= 0.1674231: NOT.QSO (359.4)
##             redshift > 0.1674231:
##             :...alpha > 344.8588: NOT.QSO (84.4)
##                 alpha <= 344.8588:
##                 :...i > 20.22575: NOT.QSO (75)
##                     i <= 20.22575:
##                     :...redshift > 0.7549464: QSO (33.9/8)
##                         redshift <= 0.7549464:
##                         :...u > 23.7849: QSO (43.6/12.4)
##                             u <= 23.7849:
##                             :...g > 22.02032: NOT.QSO (165.9)
##                                 g <= 22.02032:
##                                 :...u > 22.80464: NOT.QSO (81.8)
##                                     u <= 22.80464:
##                                     :...u > 22.7181: QSO (51.1/15.3)
##                                         u <= 22.7181:
##                                         :...u > 22.45663: NOT.QSO (63)
##                                             u <= 22.45663:
##                                             :...u > 22.4217: QSO (37/7.6)
##                                                 u <= 22.4217:
##                                                 :...u > 22.21124: NOT.QSO (53.4)
##                                                     u <= 22.21124:
##                                                     :...z > 19.41521: QSO (102.6/25.4)
##                                                         z <= 19.41521: [S2]
## 
## SubTree [S1]
## 
## cam_col6 > 0: NOT.QSO (31.4/0.2)
## cam_col6 <= 0:
## :...spec_obj_ID <= 8.147113e+18: QSO (184.6/59.3)
##     spec_obj_ID > 8.147113e+18: NOT.QSO (382.9/147.2)
## 
## SubTree [S2]
## 
## u > 21.16444: NOT.QSO (109.3)
## u <= 21.16444:
## :...redshift <= 0.534772: NOT.QSO (302.3/92.2)
##     redshift > 0.534772: QSO (18.7)
## 
## -----  Trial 8:  -----
## 
## Decision tree:
## 
## redshift <= 0.8952028:
## :...u > 22.05367:
## :   :...g > 25.1582: QSO (52.4/22.8)
## :   :   g <= 25.1582:
## :   :   :...redshift <= 0.2390244: NOT.QSO (198.3/68.6)
## :   :       redshift > 0.2390244:
## :   :       :...g > 22.97449: NOT.QSO (318)
## :   :           g <= 22.97449:
## :   :           :...i <= 20.52125: NOT.QSO (899/55.5)
## :   :               i > 20.52125:
## :   :               :...spec_obj_ID <= 8.147113e+18: QSO (70.7/13.7)
## :   :                   spec_obj_ID > 8.147113e+18: NOT.QSO (238.2/47.3)
## :   u <= 22.05367:
## :   :...u > 22.02661: QSO (36.1/0.4)
## :       u <= 22.02661:
## :       :...u > 21.93472: NOT.QSO (83.6)
## :           u <= 21.93472:
## :           :...redshift <= 0.1674231: NOT.QSO (308.3/10.1)
## :               redshift > 0.1674231:
## :               :...u <= 19.15346: QSO (55.8)
## :                   u > 19.15346:
## :                   :...i <= 17.28261: NOT.QSO (71.6)
## :                       i > 17.28261:
## :                       :...g > 21.76308: NOT.QSO (26)
## :                           g <= 21.76308:
## :                           :...redshift > 0.7179198: QSO (110.5/6.7)
## :                               redshift <= 0.7179198:
## :                               :...delta <= 1.19691: NOT.QSO (97.5/16.2)
## :                                   delta > 1.19691:
## :                                   :...delta <= 2.444826: QSO (26/0.1)
## :                                       delta > 2.444826: [S1]
## redshift > 0.8952028:
## :...redshift > 1.667782: QSO (116.2)
##     redshift <= 1.667782:
##     :...u <= 22.04539: QSO (264.6/25.2)
##         u > 22.04539:
##         :...spec_obj_ID > 1.178267e+19: QSO (85.3/8.5)
##             spec_obj_ID <= 1.178267e+19:
##             :...run_ID5000 - 6000 > 0: NOT.QSO (39.3/3.4)
##                 run_ID5000 - 6000 <= 0:
##                 :...spec_obj_ID > 1.174672e+19: NOT.QSO (26.4)
##                     spec_obj_ID <= 1.174672e+19:
##                     :...i > 22.52137: QSO (32.3)
##                         i <= 22.52137:
##                         :...spec_obj_ID > 1.063662e+19: QSO (34.7/0.8)
##                             spec_obj_ID <= 1.063662e+19:
##                             :...spec_obj_ID > 1.049904e+19: NOT.QSO (52.1)
##                                 spec_obj_ID <= 1.049904e+19:
##                                 :...r > 22.19364: NOT.QSO (46)
##                                     r <= 22.19364:
##                                     :...z <= 19.65414: NOT.QSO (17.3/1.9)
##                                         z > 19.65414:
##                                         :...delta <= 50.90433: QSO (257.4/45)
##                                             delta > 50.90433: NOT.QSO (68.8/20.6)
## 
## SubTree [S1]
## 
## run_ID1000 - 2000 <= 0: QSO (345.1/145.2)
## run_ID1000 - 2000 > 0: NOT.QSO (18.5)
## 
## -----  Trial 9:  -----
## 
## Decision tree:
## 
## redshift > 0.9787375:
## :...spec_obj_ID <= 8.457806e+18: QSO (161.2)
## :   spec_obj_ID > 8.457806e+18:
## :   :...u <= 22.357: QSO (337.5/44.5)
## :       u > 22.357: NOT.QSO (246.3/107.3)
## redshift <= 0.9787375:
## :...z <= 19.97678:
##     :...u > 22.75362: NOT.QSO (727.5/6.7)
##     :   u <= 22.75362:
##     :   :...u > 22.75153: QSO (27.3)
##     :       u <= 22.75153:
##     :       :...redshift <= 0.1674231: NOT.QSO (240.3)
##     :           redshift > 0.1674231:
##     :           :...g <= 17.89093: QSO (25)
##     :               g > 17.89093: NOT.QSO (1117.4/213.3)
##     z > 19.97678:
##     :...z > 22.3164: NOT.QSO (59.7)
##         z <= 22.3164:
##         :...u > 25.76417: NOT.QSO (39.9)
##             u <= 25.76417:
##             :...u > 25.68847: QSO (29.3)
##                 u <= 25.68847:
##                 :...g > 22.24555: NOT.QSO (303.9/59.4)
##                     g <= 22.24555:
##                     :...redshift <= 0.2236984: NOT.QSO (89.5/6.6)
##                         redshift > 0.2236984:
##                         :...redshift <= 0.2390244: QSO (33.5)
##                             redshift > 0.2390244:
##                             :...r > 21.92936: NOT.QSO (62.6)
##                                 r <= 21.92936:
##                                 :...redshift <= 0.4134484: NOT.QSO (37.7/3.5)
##                                     redshift > 0.4134484: QSO (447.4/130.6)
## 
## 
## Evaluation on training data (4000 cases):
## 
## Trial        Decision Tree   
## -----      ----------------  
##    Size      Errors  
## 
##    0     18   92( 2.3%)
##    1     17  331( 8.3%)
##    2     19  221( 5.5%)
##    3     22  195( 4.9%)
##    4     23  225( 5.6%)
##    5     21  171( 4.3%)
##    6     25  228( 5.7%)
##    7     36  180( 4.5%)
##    8     29  148( 3.7%)
##    9     17  180( 4.5%)
## boost             34( 0.8%)   <<
## 
## 
##     (a)   (b)    <-classified as
##    ----  ----
##    3226     7    (a): class NOT.QSO
##      27   740    (b): class QSO
## 
## 
##  Attribute usage:
## 
##  100.00% u
##  100.00% z
##  100.00% redshift
##   97.68% i
##   76.22% g
##   66.32% r
##   62.88% alpha
##   46.55% spec_obj_ID
##   39.78% run_ID8000 - 9000
##   36.13% delta
##   31.45% run_ID1000 - 2000
##   26.45% run_ID7000 - 8000
##   16.80% MJD
##    7.80% run_ID5000 - 6000
##    4.68% cam_col6
##    1.92% run_ID2000 - 3000
## 
## 
## Time: 0.5 secs

Now that we have our model trained, we want to predict our test dataset.

c50.pred <- predict(fit.c50, newdata=test_data)
cm = confusionMatrix(c50.pred, test_data$class)
cm

## Confusion Matrix and Statistics
## 
##           Reference
## Prediction NOT.QSO  QSO
##    NOT.QSO    4798  102
##    QSO          51 1048
##                                           
##                Accuracy : 0.9745          
##                  95% CI : (0.9702, 0.9783)
##     No Information Rate : 0.8083          
##     P-Value [Acc > NIR] : < 2.2e-16       
##                                           
##                   Kappa : 0.9163          
##                                           
##  Mcnemar's Test P-Value : 5.294e-05       
##                                           
##             Sensitivity : 0.9895          
##             Specificity : 0.9113          
##          Pos Pred Value : 0.9792          
##          Neg Pred Value : 0.9536          
##              Prevalence : 0.8083          
##          Detection Rate : 0.7998          
##    Detection Prevalence : 0.8168          
##       Balanced Accuracy : 0.9504          
##                                           
##        'Positive' Class : NOT.QSO         
## 

accuracy = cm$overall["Accuracy"]
profit = EconomicProfit(data = data.frame(pred  = c50.pred, obs = test_data$class))              
profit

## EconomicProfit 
##       0.225771

In terms of accuracy, it is a really strong model with 0.9744957, but we are interested in the economic profit. In this model, the economic profit is 0.225771, which does improve the economic of our best SVM model.

We are, once again, computing the probabilities (fraction of samples of the same class in a leaf) in order to be able to change the threshold manually and try to improve more our model. We once again try with 0.2 as the threshold.

c50.Prob = predict(fit.c50, test_data, type="prob")
head(c50.Prob)

##        NOT.QSO        QSO
## 2370 1.0000000 0.00000000
## 5274 0.9129324 0.08706757
## 9291 0.0000000 1.00000000
## 8827 1.0000000 0.00000000
## 356  0.9263597 0.07364031
## 7701 1.0000000 0.00000000

threshold = 0.2
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2227788

With this threshold it is actually a bit worse, the economic profit is lower, 0.2227788. We will try with a different threshold, a little higher.

threshold = 0.3
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)
accuracy = CM$overall["Accuracy"]
profit <- sum(profit.unit*CM$table)/sum(CM$table)
profit

## [1] 0.2254709

This does actually improve, the economic profit is 0.2254709 and the accuracy is 0.9594932. We are going to follow the same structure as before, so we are going to compute the ROC, but include the 2 previous models to compare them too.

roc.dt=roc(test_data$class ~ c50.Prob[,2])

## Setting levels: control = NOT.QSO, case = QSO

## Setting direction: controls < cases

plot(roc.knn, col="red",print.thres=TRUE)
plot(roc.svm, add=TRUE, col='blue',print.thres=TRUE)
plot(roc.dt, add=TRUE, col='green',print.thres=TRUE)
legend("bottomright", legend=c("knn", "svm", "dt"), col=c("red", "blue", "green"), lwd=2)

optimal <- as.numeric(coords(roc.dt, "best", ret = "threshold"))

auc = roc.dt$auc

We plotted the three model seen until now (knn, svm and decision trees) and we can see that the best is decision trees followed by svm and then, the worst, knn. The optimal threshold computed by the ROC for our decision tree model is 0.3655297 and the resulting AUC is 0.9813301, which is an incredibly good model. As seen before, maybe this ‘optimal’ threshold is not optimal according to our economic profit, so we are going to check if it improves or not.

c50.Prob = predict(fit.c50, test_data, type="prob")
threshold = optimal
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2274879

In this case, it actually does improve the economic profit to 0.2274879. From all the models explored until now, the best performing one yet, in terms of economic profit, is the decision tree with probabilities with 0.3655297 as the threshold. However, we do not know if it the optimal according to our economic aspect, because the ROC does not take it into account as we previously said. We compute the optimal and make the final prediction.

Since the ROC does not take into account the economic nature of our problem and we compute the optimal threshold with the following loop, we are not going to keep using the ROC.

profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid <- best_hyperparameters

seq_values <- seq(0.05, 0.45, 0.05)

# Append 0.22 to the sequence
seq_values <- c(seq_values, optimal)

j <- 0
for (threshold in seq_values){
  
  j <- j + 1
  #cat(j)
  for(i in 1:15){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    fit.c50 <- train(class ~.,
                data=train,
                method="C5.0",
                metric="EconomicProfit",
                tuneGrid = grid,
                trControl = ctrl)
    
    dtProb = predict(fit.c50, test, type="prob")
    dtPred = rep("NOT.QSO", nrow(test))
    dtPred[which(dtProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(dtPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
    
  }
}

## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
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## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
## + Fold1: trials=10, model=tree, winnow=TRUE 
## - Fold1: trials=10, model=tree, winnow=TRUE 
## + Fold2: trials=10, model=tree, winnow=TRUE 
## - Fold2: trials=10, model=tree, winnow=TRUE 
## + Fold3: trials=10, model=tree, winnow=TRUE 
## - Fold3: trials=10, model=tree, winnow=TRUE 
## + Fold4: trials=10, model=tree, winnow=TRUE 
## - Fold4: trials=10, model=tree, winnow=TRUE 
## + Fold5: trials=10, model=tree, winnow=TRUE 
## - Fold5: trials=10, model=tree, winnow=TRUE 
## Aggregating results
## Fitting final model on full training set
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## Fitting final model on full training set

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq_values, col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] 0.1541476 0.2042726 0.2092330 0.2191747 0.2224260 0.2238224 0.2244894
##  [8] 0.2229471 0.2229054 0.2231138

The optimal threshold is computed and then, we obtain the final predictions and corresponding economic profit.

indexthr = which.max(medians)
threshold = seq_values[indexthr]
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2267878

This is our best model to this moment, but we are not satisfied yet, we know a bunch of more models with machine learning, so we are going to study them too, random forest first. We store its profit and optimal threshold too.

dtprofit = profit
dtoptimal = threshold

Variable importance and partial dependencies

We compute the variable importance.

dt_imp <- varImp(fit.c50, scale = F)
plot(dt_imp, scales = list(y = list(cex = .95)))

We see clearly that the most important variables are redshift (as always), i and u, with the same importance all of them.

partial(fit.c50, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(fit.c50, pred.var = "i", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(fit.c50, pred.var = "u", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

From the redshift graph we can draw the same conclusions as before, but seeing that the probability does not increase to 1, but only to 0.9. The variable i shows that with values less than 20 they are not likely to be QSO and with higher than 23 they are QSO. Between 20 and 23 the higher the more probable it is QSO, with a decrease in probability in the middle. The variable u works as the contrary, the higher the value, the lower the probability of being classified as QSO.

Random Forest

Random Forest is a popular ensemble learning technique used for both classification and regression tasks. It operates by building multiple decision trees during the training phase and then combining their predictions to improve accuracy and reduce overfitting.

However, Random Forest may not be as interpretable as individual decision trees, especially when dealing with a large number of trees. Additionally, training a Random Forest model can be computationally expensive, especially with a large number of trees and features. Despite these drawbacks, Random Forest remains one of the most popular and widely used machine learning algorithms due to its excellent performance across various tasks and datasets.

Once again, we use caret to train the model with out economic profit in mind.

rf.train <- train(class ~., 
                  method = "rf", 
                  data = train_data,
                  preProcess = c("center", "scale"),
                  ntree = 200,
                  cutoff=c(0.7,0.3),
                  tuneGrid = expand.grid(mtry=c(6,7,8,9,10)), 
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)

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## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 8 on full training set

rf.train

## Random Forest 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3200, 3200, 3201, 3199, 3200 
## Resampling results across tuning parameters:
## 
##   mtry  EconomicProfit
##    6    0.2285901     
##    7    0.2275645     
##    8    0.2273895     
##    9    0.2278392     
##   10    0.2282391     
## 
## EconomicProfit was used to select the optimal model using the smallest value.
## The final value used for the model was mtry = 8.

best_hyperparameters <- rf.train$bestTune
best_row <- rf.train$results[rf.train$results$mtry == best_hyperparameters$mtry, ]
profit <- best_row$EconomicProfit

We see that the hyper-parameter with highest economic profit is mtry = 8, which we are later going to use, so we store it. We use now this trained model to obtain our predictions and obtain the economic profit to be able to compare.

rfhyp = best_hyperparameters

rfPred = predict(rf.train, newdata=test_data)
CM = confusionMatrix(factor(rfPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.2286964

This is the best model yet, with the highest economic profit 0.2286964.

We try to improve it even more changing the threshold (first manually and then obtaining the optimal one).

Sometimes, the threshold in the Bayes rule is more important than hyper-parameters in the ML tools:

threshold = 0.2
rfProb = predict(rf.train, newdata=test_data, type="prob")
rfPred = rep("NOT.QSO", nrow(test_data))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(rfPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.2289548

It got worse with this one to 0.2289548, let’s compute the optimal one.

profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = best_hyperparameters

j <- 0
for (threshold in seq(0.05,0.5,0.05)){
  
  j <- j + 1
  #cat(j)
  for(i in 1:15){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    rf.train <- train(class ~., 
                  method = "rf", 
                  data = train,
                  preProcess = c("center", "scale"),
                  ntree = 200,
                  cutoff=c(0.7,0.3),
                  tuneGrid = grid, 
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)
    
    rfProb = predict(rf.train, test, type="prob")
    rfPred = rep("NOT.QSO", nrow(test))
    rfPred[which(rfProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(svmPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
  }
}

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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Fitting final model on full training set
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## Fitting final model on full training set
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## Aggregating results
## Fitting final model on full training set
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## Fitting final model on full training set
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## + Fold5: mtry=8 
## - Fold5: mtry=8 
## Aggregating results
## Fitting final model on full training set
## + Fold1: mtry=8 
## - Fold1: mtry=8 
## + Fold2: mtry=8 
## - Fold2: mtry=8 
## + Fold3: mtry=8 
## - Fold3: mtry=8 
## + Fold4: mtry=8 
## - Fold4: mtry=8 
## + Fold5: mtry=8 
## - Fold5: mtry=8 
## Aggregating results
## Fitting final model on full training set
## + Fold1: mtry=8 
## - Fold1: mtry=8 
## + Fold2: mtry=8 
## - Fold2: mtry=8 
## + Fold3: mtry=8 
## - Fold3: mtry=8 
## + Fold4: mtry=8 
## - Fold4: mtry=8 
## + Fold5: mtry=8 
## - Fold5: mtry=8 
## Aggregating results
## Fitting final model on full training set
## + Fold1: mtry=8 
## - Fold1: mtry=8 
## + Fold2: mtry=8 
## - Fold2: mtry=8 
## + Fold3: mtry=8 
## - Fold3: mtry=8 
## + Fold4: mtry=8 
## - Fold4: mtry=8 
## + Fold5: mtry=8 
## - Fold5: mtry=8 
## Aggregating results
## Fitting final model on full training set
## + Fold1: mtry=8 
## - Fold1: mtry=8 
## + Fold2: mtry=8 
## - Fold2: mtry=8 
## + Fold3: mtry=8 
## - Fold3: mtry=8 
## + Fold4: mtry=8 
## - Fold4: mtry=8 
## + Fold5: mtry=8 
## - Fold5: mtry=8 
## Aggregating results
## Fitting final model on full training set

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq(0.05,0.5,0.05),col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] 0.005356398 0.007357232 0.006023343 0.004689454 0.004689454 0.009358066
##  [7] 0.008024177 0.004022509 0.004022509 0.004689454

We make our final predictions of the random forest model with the optimal threshold which we compute.

indexthr = which.max(medians)
threshold = seq(0.05,0.5,0.05)[indexthr]
rfProb = predict(rf.train, newdata=test_data, type="prob")
rfPred = rep("NOT.QSO", nrow(test_data))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(rfPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.2245791

We obtain an economic profit of 0.2245791, that is slightly worse than the decision trees one. Our best model yet is with decision trees. We store its profit and optimal threshold.

rfprofit = profit
rfoptimal = threshold

Variable importance and partial dependencies

Let`s see which are the most explanatory variables.

rf_imp <- varImp(rf.train, scale = F)
plot(rf_imp, scales = list(y = list(cex = .95)))

partial(rf.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

We draw the same conclusion as always, but like in decision trees, the probability only increases until 0.9.

Gradient boosting

Gradient Boosting is a powerful machine learning technique used for both regression and classification tasks. It works by building a series of weak learners (typically decision trees) in a sequential manner, where each new learner corrects the errors made by the previous ones. Gradient Boosting combines the predictions of multiple weak learners to create a strong ensemble model.

Gradient Boosting has high predictive accuracy , it handles mixed data types (a mixture of numerical and categorical features), it automatically handles missing values (learns from data with missing values without imputation) and provides feature importance.

However, Gradient Boosting can be sensitive to hyperparameters and may require careful tuning to prevent overfitting. It can also be computationally expensive and slower to train compared to other algorithms. Despite these challenges, Gradient Boosting remains a popular and effective choice for various machine learning tasks.

We are going to use the caret package to train a model that uses gradient boosting and takes into account the economic nature of our task. Firstly, we set the very wide grid for hyper-parameter tuning and then, we train introducing this grid.

xgb_grid = expand.grid(
  nrounds = c(500, 600, 700, 800, 1000),
  eta = c(0.01, 0.001), 
  max_depth = c(2, 3, 4, 5, 6),
  gamma = 1,
  colsample_bytree = c(0.2, 0.25, 0.3, 0.35, 0.4),
  min_child_weight = c(1, 2, 3, 4, 5),
  subsample = 1
)

Then, train.

xgb.train = train(class ~ .,
                  data=train_data,
                  trControl = ctrl,
                  metric="EconomicProfit",
                  maximize = F,
                  tuneGrid = xgb_grid,
                  preProcess = c("center", "scale"),
                  method = "xgbTree"
)

## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:39:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:39:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:40:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:41:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:42:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:43:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:44:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:44:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:45:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:46:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:46:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:48:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:50:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:51:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:52:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:53:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:54:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:57:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold1: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:58:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [19:59:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:01:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:02:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:03:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:04:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:05:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:06:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:07:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:08:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:09:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:11:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:12:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:13:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:13:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:14:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:15:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:17:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:17:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:18:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:19:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:20:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:21:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold2: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:23:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:25:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:26:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:27:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:27:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:28:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:29:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:30:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:30:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:31:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:32:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:33:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:34:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:35:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:36:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:37:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:38:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:39:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:40:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:40:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:41:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:42:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:43:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold3: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:44:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:45:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:46:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:47:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:48:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:49:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:50:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:51:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:52:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:53:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:54:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:55:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:56:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:56:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:56:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:57:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:58:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [20:59:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:00:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:01:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:02:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:03:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:04:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold4: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:05:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:06:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:07:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:32] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:08:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:20] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:09:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:10:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:11:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:12:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:36] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:13:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:14:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:15:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:04] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.001, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:29] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:47] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:54] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:16:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:07] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=2, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:25] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:46] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:17:59] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:12] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:16] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=3, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:41] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:42] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:49] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:18:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:26] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:35] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:52] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:19:56] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:01] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:10] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:14] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=4, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:19] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:24] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:38] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:43] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:48] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:53] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:20:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:02] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:08] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:13] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:18] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:21:55] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:00] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:06] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:22] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=5, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:28] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:34] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:40] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.20, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:22:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:03] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:09] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:15] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:21] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.25, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:27] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:33] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:39] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:45] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:51] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.30, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:23:58] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:05] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:11] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:17] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:23] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.35, min_child_weight=5, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:30] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:31] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=1, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:37] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=2, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:44] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=3, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:50] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=4, subsample=1, nrounds=1000 
## + Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## [21:24:57] WARNING: src/c_api/c_api.cc:935: `ntree_limit` is deprecated, use `iteration_range` instead.
## - Fold5: eta=0.010, max_depth=6, gamma=1, colsample_bytree=0.40, min_child_weight=5, subsample=1, nrounds=1000 
## Aggregating results
## Selecting tuning parameters
## Fitting nrounds = 500, max_depth = 2, eta = 0.001, gamma = 1, colsample_bytree = 0.2, min_child_weight = 5, subsample = 1 on full training set

best_hyperparameters = xgb.train$bestTune

xgb.train

## eXtreme Gradient Boosting 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3199, 3200, 3201, 3200, 3200 
## Resampling results across tuning parameters:
## 
##   eta    max_depth  colsample_bytree  min_child_weight  nrounds  EconomicProfit
##   0.001  2          0.20              1                  500     0.09554571    
##   0.001  2          0.20              1                  600     0.10304728    
##   0.001  2          0.20              1                  700     0.10858751    
##   0.001  2          0.20              1                  800     0.11466408    
##   0.001  2          0.20              1                 1000     0.12497620    
##   0.001  2          0.20              2                  500     0.09336173    
##   0.001  2          0.20              2                  600     0.09742345    
##   0.001  2          0.20              2                  700     0.10680080    
##   0.001  2          0.20              2                  800     0.11202463    
##   0.001  2          0.20              2                 1000     0.12006331    
##   0.001  2          0.20              3                  500     0.09734181    
##   0.001  2          0.20              3                  600     0.10336876    
##   0.001  2          0.20              3                  700     0.10859377    
##   0.001  2          0.20              3                  800     0.11234377    
##   0.001  2          0.20              3                 1000     0.12122854    
##   0.001  2          0.20              4                  500     0.10077462    
##   0.001  2          0.20              4                  600     0.10412579    
##   0.001  2          0.20              4                  700     0.10809962    
##   0.001  2          0.20              4                  800     0.11685080    
##   0.001  2          0.20              4                 1000     0.12520120    
##   0.001  2          0.20              5                  500     0.09001329    
##   0.001  2          0.20              5                  600     0.09836290    
##   0.001  2          0.20              5                  700     0.10327696    
##   0.001  2          0.20              5                  800     0.10859025    
##   0.001  2          0.20              5                 1000     0.11881487    
##   0.001  2          0.25              1                  500     0.12738491    
##   0.001  2          0.25              1                  600     0.13082436    
##   0.001  2          0.25              1                  700     0.13479975    
##   0.001  2          0.25              1                  800     0.13823686    
##   0.001  2          0.25              1                 1000     0.14167436    
##   0.001  2          0.25              2                  500     0.12841343    
##   0.001  2          0.25              2                  600     0.13278843    
##   0.001  2          0.25              2                  700     0.13426187    
##   0.001  2          0.25              2                  800     0.13605132    
##   0.001  2          0.25              2                 1000     0.13917554    
##   0.001  2          0.25              3                  500     0.12256995    
##   0.001  2          0.25              3                  600     0.12551877    
##   0.001  2          0.25              3                  700     0.13105444    
##   0.001  2          0.25              3                  800     0.13636577    
##   0.001  2          0.25              3                 1000     0.14042593    
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##   0.001  2          0.25              4                  600     0.13332436    
##   0.001  2          0.25              4                  700     0.13707358    
##   0.001  2          0.25              4                  800     0.13957241    
##   0.001  2          0.25              4                 1000     0.14229819    
##   0.001  2          0.25              5                  500     0.12488724    
##   0.001  2          0.25              5                  600     0.13051264    
##   0.001  2          0.25              5                  700     0.13301264    
##   0.001  2          0.25              5                  800     0.13667397    
##   0.001  2          0.25              5                 1000     0.14229858    
##   0.001  2          0.30              1                  500     0.14417476    
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##   0.001  2          0.30              1                  700     0.14730015    
##   0.001  2          0.30              1                  800     0.15042515    
##   0.001  2          0.30              1                 1000     0.15315015    
##   0.001  2          0.30              2                  500     0.14542749    
##   0.001  2          0.30              2                  600     0.14783999    
##   0.001  2          0.30              2                  700     0.14698882    
##   0.001  2          0.30              2                  800     0.15073726    
##   0.001  2          0.30              2                 1000     0.15354976    
##   0.001  2          0.30              3                  500     0.14752476    
##   0.001  2          0.30              3                  600     0.14940015    
##   0.001  2          0.30              3                  700     0.14940015    
##   0.001  2          0.30              3                  800     0.15189976    
##   0.001  2          0.30              3                 1000     0.15292476    
##   0.001  2          0.30              4                  500     0.14533687    
##   0.001  2          0.30              4                  600     0.14783687    
##   0.001  2          0.30              4                  700     0.14783648    
##   0.001  2          0.30              4                  800     0.15096148    
##   0.001  2          0.30              4                 1000     0.15408726    
##   0.001  2          0.30              5                  500     0.13948959    
##   0.001  2          0.30              5                  600     0.14355093    
##   0.001  2          0.30              5                  700     0.14730015    
##   0.001  2          0.30              5                  800     0.14948804    
##   0.001  2          0.30              5                 1000     0.15252476    
##   0.001  2          0.35              1                  500     0.15775133    
##   0.001  2          0.35              1                  600     0.15900055    
##   0.001  2          0.35              1                  700     0.16047555    
##   0.001  2          0.35              1                  800     0.16266071    
##   0.001  2          0.35              1                 1000     0.17069821    
##   0.001  2          0.35              2                  500     0.15618844    
##   0.001  2          0.35              2                  600     0.15618765    
##   0.001  2          0.35              2                  700     0.15859937    
##   0.001  2          0.35              2                  800     0.16172399    
##   0.001  2          0.35              2                 1000     0.16609587    
##   0.001  2          0.35              3                  500     0.15587633    
##   0.001  2          0.35              3                  600     0.15618804    
##   0.001  2          0.35              3                  700     0.15703883    
##   0.001  2          0.35              3                  800     0.15922633    
##   0.001  2          0.35              3                 1000     0.16360016    
##   0.001  2          0.35              4                  500     0.15618961    
##   0.001  2          0.35              4                  600     0.15775289    
##   0.001  2          0.35              4                  700     0.15953922    
##   0.001  2          0.35              4                  800     0.16110055    
##   0.001  2          0.35              4                 1000     0.16476110    
##   0.001  2          0.35              5                  500     0.15556187    
##   0.001  2          0.35              5                  600     0.15775054    
##   0.001  2          0.35              5                  700     0.15891187    
##   0.001  2          0.35              5                  800     0.16109781    
##   0.001  2          0.35              5                 1000     0.16632047    
##   0.001  2          0.40              1                  500     0.16555953    
##   0.001  2          0.40              1                  600     0.16609860    
##   0.001  2          0.40              1                  700     0.17515564    
##   0.001  2          0.40              1                  800     0.17890525    
##   0.001  2          0.40              1                 1000     0.18350759    
##   0.001  2          0.40              2                  500     0.16672242    
##   0.001  2          0.40              2                  600     0.17412946    
##   0.001  2          0.40              2                  700     0.17529207    
##   0.001  2          0.40              2                  800     0.17497957    
##   0.001  2          0.40              2                 1000     0.18060642    
##   0.001  2          0.40              3                  500     0.16557087    
##   0.001  2          0.40              3                  600     0.16985719    
##   0.001  2          0.40              3                  700     0.17579196    
##   0.001  2          0.40              3                  800     0.18172791    
##   0.001  2          0.40              3                 1000     0.18623689    
##   0.001  2          0.40              4                  500     0.16149625    
##   0.001  2          0.40              4                  600     0.16390993    
##   0.001  2          0.40              4                  700     0.16734665    
##   0.001  2          0.40              4                  800     0.17203884    
##   0.001  2          0.40              4                 1000     0.18154979    
##   0.001  2          0.40              5                  500     0.16181422    
##   0.001  2          0.40              5                  600     0.16766891    
##   0.001  2          0.40              5                  700     0.16891735    
##   0.001  2          0.40              5                  800     0.17578923    
##   0.001  2          0.40              5                 1000     0.18561189    
##   0.001  3          0.20              1                  500     0.12242698    
##   0.001  3          0.20              1                  600     0.12742776    
##   0.001  3          0.20              1                  700     0.12961565    
##   0.001  3          0.20              1                  800     0.13336566    
##   0.001  3          0.20              1                 1000     0.13805238    
##   0.001  3          0.20              2                  500     0.12898909    
##   0.001  3          0.20              2                  600     0.13242581    
##   0.001  3          0.20              2                  700     0.13367582    
##   0.001  3          0.20              2                  800     0.13523832    
##   0.001  3          0.20              2                 1000     0.14046332    
##   0.001  3          0.20              3                  500     0.12064104    
##   0.001  3          0.20              3                  600     0.12649143    
##   0.001  3          0.20              3                  700     0.12930315    
##   0.001  3          0.20              3                  800     0.13305276    
##   0.001  3          0.20              3                 1000     0.13742777    
##   0.001  3          0.20              4                  500     0.12023674    
##   0.001  3          0.20              4                  600     0.12586370    
##   0.001  3          0.20              4                  700     0.12961331    
##   0.001  3          0.20              4                  800     0.13305120    
##   0.001  3          0.20              4                 1000     0.13680160    
##   0.001  3          0.20              5                  500     0.10845197    
##   0.001  3          0.20              5                  600     0.11867932    
##   0.001  3          0.20              5                  700     0.12149260    
##   0.001  3          0.20              5                  800     0.12524104    
##   0.001  3          0.20              5                 1000     0.13180159    
##   0.001  3          0.25              1                  500     0.14474965    
##   0.001  3          0.25              1                  600     0.14528715    
##   0.001  3          0.25              1                  700     0.14653637    
##   0.001  3          0.25              1                  800     0.15028520    
##   0.001  3          0.25              1                 1000     0.15747231    
##   0.001  3          0.25              2                  500     0.14193910    
##   0.001  3          0.25              2                  600     0.14443832    
##   0.001  3          0.25              2                  700     0.14568793    
##   0.001  3          0.25              2                  800     0.14662543    
##   0.001  3          0.25              2                 1000     0.15395122    
##   0.001  3          0.25              3                  500     0.14506293    
##   0.001  3          0.25              3                  600     0.14693793    
##   0.001  3          0.25              3                  700     0.14818832    
##   0.001  3          0.25              3                  800     0.15037465    
##   0.001  3          0.25              3                 1000     0.15872466    
##   0.001  3          0.25              4                  500     0.14242465    
##   0.001  3          0.25              4                  600     0.14367504    
##   0.001  3          0.25              4                  700     0.14773793    
##   0.001  3          0.25              4                  800     0.14890082    
##   0.001  3          0.25              4                 1000     0.15497778    
##   0.001  3          0.25              5                  500     0.14086215    
##   0.001  3          0.25              5                  600     0.14546137    
##   0.001  3          0.25              5                  700     0.14639926    
##   0.001  3          0.25              5                  800     0.14889848    
##   0.001  3          0.25              5                 1000     0.15318637    
##   0.001  3          0.30              1                  500     0.15364184    
##   0.001  3          0.30              1                  600     0.15792973    
##   0.001  3          0.30              1                  700     0.16042934    
##   0.001  3          0.30              1                  800     0.16417583    
##   0.001  3          0.30              1                 1000     0.17136177    
##   0.001  3          0.30              2                  500     0.15667632    
##   0.001  3          0.30              2                  600     0.16127594    
##   0.001  3          0.30              2                  700     0.16939782    
##   0.001  3          0.30              2                  800     0.17096149    
##   0.001  3          0.30              2                 1000     0.17962400    
##   0.001  3          0.30              3                  500     0.16207310    
##   0.001  3          0.30              3                  600     0.16863482    
##   0.001  3          0.30              3                  700     0.17167087    
##   0.001  3          0.30              3                  800     0.17385954    
##   0.001  3          0.30              3                 1000     0.17721072    
##   0.001  3          0.30              4                  500     0.15247660    
##   0.001  3          0.30              4                  600     0.15488832    
##   0.001  3          0.30              4                  700     0.15957505    
##   0.001  3          0.30              4                  800     0.16238950    
##   0.001  3          0.30              4                 1000     0.17136060    
##   0.001  3          0.30              5                  500     0.16010602    
##   0.001  3          0.30              5                  600     0.16260680    
##   0.001  3          0.30              5                  700     0.16783376    
##   0.001  3          0.30              5                  800     0.16948415    
##   0.001  3          0.30              5                 1000     0.17823337    
##   0.001  3          0.35              1                  500     0.18788896    
##   0.001  3          0.35              1                  600     0.19007685    
##   0.001  3          0.35              1                  700     0.19092646    
##   0.001  3          0.35              1                  800     0.19163924    
##   0.001  3          0.35              1                 1000     0.19266424    
##   0.001  3          0.35              2                  500     0.18945146    
##   0.001  3          0.35              2                  600     0.18922646    
##   0.001  3          0.35              2                  700     0.19016396    
##   0.001  3          0.35              2                  800     0.19070146    
##   0.001  3          0.35              2                 1000     0.19453924    
##   0.001  3          0.35              3                  500     0.18998935    
##   0.001  3          0.35              3                  600     0.19217646    
##   0.001  3          0.35              3                  700     0.19342685    
##   0.001  3          0.35              3                  800     0.19373935    
##   0.001  3          0.35              3                 1000     0.19538963    
##   0.001  3          0.35              4                  500     0.18507685    
##   0.001  3          0.35              4                  600     0.18788974    
##   0.001  3          0.35              4                  700     0.18953935    
##   0.001  3          0.35              4                  800     0.19132685    
##   0.001  3          0.35              4                 1000     0.19329002    
##   0.001  3          0.35              5                  500     0.19101474    
##   0.001  3          0.35              5                  600     0.19226474    
##   0.001  3          0.35              5                  700     0.19288974    
##   0.001  3          0.35              5                  800     0.19382685    
##   0.001  3          0.35              5                 1000     0.19570107    
##   0.001  3          0.40              1                  500     0.19476553    
##   0.001  3          0.40              1                  600     0.19592803    
##   0.001  3          0.40              1                  700     0.19749014    
##   0.001  3          0.40              1                  800     0.19905264    
##   0.001  3          0.40              1                 1000     0.20084014    
##   0.001  3          0.40              2                  500     0.19413818    
##   0.001  3          0.40              2                  600     0.19561318    
##   0.001  3          0.40              2                  700     0.19686435    
##   0.001  3          0.40              2                  800     0.19873974    
##   0.001  3          0.40              2                 1000     0.20052685    
##   0.001  3          0.40              3                  500     0.19288740    
##   0.001  3          0.40              3                  600     0.19530029    
##   0.001  3          0.40              3                  700     0.19623818    
##   0.001  3          0.40              3                  800     0.19748818    
##   0.001  3          0.40              3                 1000     0.19927568    
##   0.001  3          0.40              4                  500     0.19445107    
##   0.001  3          0.40              4                  600     0.19601357    
##   0.001  3          0.40              4                  700     0.19717646    
##   0.001  3          0.40              4                  800     0.19873896    
##   0.001  3          0.40              4                 1000     0.20177764    
##   0.001  3          0.40              5                  500     0.19547607    
##   0.001  3          0.40              5                  600     0.19547646    
##   0.001  3          0.40              5                  700     0.19632646    
##   0.001  3          0.40              5                  800     0.19695146    
##   0.001  3          0.40              5                 1000     0.19873896    
##   0.001  4          0.20              1                  500     0.13470121    
##   0.001  4          0.20              1                  600     0.13626331    
##   0.001  4          0.20              1                  700     0.13751214    
##   0.001  4          0.20              1                  800     0.13970003    
##   0.001  4          0.20              1                 1000     0.14273714    
##   0.001  4          0.20              2                  500     0.12992620    
##   0.001  4          0.20              2                  600     0.13188870    
##   0.001  4          0.20              2                  700     0.13742777    
##   0.001  4          0.20              2                  800     0.14086527    
##   0.001  4          0.20              2                 1000     0.14430160    
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##   0.001  4          0.20              3                  700     0.13961214    
##   0.001  4          0.20              3                  800     0.14233753    
##   0.001  4          0.20              3                 1000     0.14452464    
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##   0.001  4          0.20              4                  600     0.13095198    
##   0.001  4          0.20              4                  700     0.13430198    
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##   0.001  4          0.20              4                 1000     0.14077777    
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##   0.001  4          0.20              5                  700     0.13501566    
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##   0.001  4          0.20              5                 1000     0.14148832    
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##   0.001  4          0.25              1                  700     0.15350122    
##   0.001  4          0.25              1                  800     0.15640200    
##   0.001  4          0.25              1                 1000     0.16256489    
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##   0.001  4          0.25              2                  700     0.15381489    
##   0.001  4          0.25              2                  800     0.15617856    
##   0.001  4          0.25              2                 1000     0.16207778    
##   0.001  4          0.25              3                  500     0.15015239    
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##   0.001  4          0.25              3                  700     0.15702270    
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##   0.001  4          0.25              3                 1000     0.16380943    
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##   0.001  4          0.25              4                  600     0.15506177    
##   0.001  4          0.25              4                  700     0.15622427    
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##   0.001  4          0.25              4                 1000     0.16372349    
##   0.001  4          0.25              5                  500     0.14773598    
##   0.001  4          0.25              5                  600     0.15139926    
##   0.001  4          0.25              5                  700     0.15577388    
##   0.001  4          0.25              5                  800     0.15827505    
##   0.001  4          0.25              5                 1000     0.16577388    
##   0.001  4          0.30              1                  500     0.16903756    
##   0.001  4          0.30              1                  600     0.17184967    
##   0.001  4          0.30              1                  700     0.17434928    
##   0.001  4          0.30              1                  800     0.17573678    
##   0.001  4          0.30              1                 1000     0.18158835    
##   0.001  4          0.30              2                  500     0.17185084    
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##   0.001  4          0.30              2                  700     0.17707311    
##   0.001  4          0.30              2                  800     0.17801139    
##   0.001  4          0.30              2                 1000     0.18457506    
##   0.001  4          0.30              3                  500     0.16934849    
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##   0.001  4          0.30              3                  700     0.17497310    
##   0.001  4          0.30              3                  800     0.17684810    
##   0.001  4          0.30              3                 1000     0.18175944    
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##   0.001  4          0.30              4                  700     0.17287701    
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##   0.001  4          0.30              5                  700     0.17301099    
##   0.001  4          0.30              5                  800     0.17653522    
##   0.001  4          0.30              5                 1000     0.17988600    
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##   0.001  4          0.35              2                  800     0.19681346    
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##   0.001  4          0.35              3                  800     0.19784081    
##   0.001  4          0.35              3                 1000     0.19994042    
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##   0.001  4          0.35              4                  800     0.19752830    
##   0.001  4          0.35              4                 1000     0.20065409    
##   0.001  4          0.35              5                  500     0.19681396    
##   0.001  4          0.35              5                  600     0.19681396    
##   0.001  4          0.35              5                  700     0.19627674    
##   0.001  4          0.35              5                  800     0.19752635    
##   0.001  4          0.35              5                 1000     0.19971463    
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##   0.001  4          0.40              1                 1000     0.20351542    
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##   0.001  4          0.40              2                  700     0.20266592    
##   0.001  4          0.40              2                  800     0.20226592    
##   0.001  4          0.40              2                 1000     0.20538936    
##   0.001  4          0.40              3                  500     0.20110420    
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##   0.001  4          0.40              3                  700     0.20244248    
##   0.001  4          0.40              3                  800     0.20266709    
##   0.001  4          0.40              3                 1000     0.20712792    
##   0.001  4          0.40              4                  500     0.20110303    
##   0.001  4          0.40              4                  600     0.20141631    
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##   0.001  4          0.40              4                  800     0.20320342    
##   0.001  4          0.40              4                 1000     0.20703964    
##   0.001  4          0.40              5                  500     0.19985224    
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##   0.001  5          0.40              5                 1000     0.20704081    
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##   0.010  2          0.35              1                  800     0.21048836    
##   0.010  2          0.35              1                 1000     0.21687587    
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##   0.010  2          0.35              2                  600     0.20526386    
##   0.010  2          0.35              2                  700     0.20665019    
##   0.010  2          0.35              2                  800     0.20915164    
##   0.010  2          0.35              2                 1000     0.21500154    
##   0.010  2          0.35              3                  500     0.20128662    
##   0.010  2          0.35              3                  600     0.20455058    
##   0.010  2          0.35              3                  700     0.20776269    
##   0.010  2          0.35              3                  800     0.21008875    
##   0.010  2          0.35              3                 1000     0.21687654    
##   0.010  2          0.35              4                  500     0.20039806    
##   0.010  2          0.35              4                  600     0.20566347    
##   0.010  2          0.35              4                  700     0.20807480    
##   0.010  2          0.35              4                  800     0.21017508    
##   0.010  2          0.35              4                 1000     0.21656326    
##   0.010  2          0.35              5                  500     0.20262480    
##   0.010  2          0.35              5                  600     0.20628847    
##   0.010  2          0.35              5                  700     0.20745097    
##   0.010  2          0.35              5                  800     0.20821375    
##   0.010  2          0.35              5                 1000     0.21585204    
##   0.010  2          0.40              1                  500     0.20347491    
##   0.010  2          0.40              1                  600     0.20682558    
##   0.010  2          0.40              1                  700     0.20861297    
##   0.010  2          0.40              1                  800     0.21218875    
##   0.010  2          0.40              1                 1000     0.21732637    
##   0.010  2          0.40              2                  500     0.20356151    
##   0.010  2          0.40              2                  600     0.20682558    
##   0.010  2          0.40              2                  700     0.20915047    
##   0.010  2          0.40              2                  800     0.21165164    
##   0.010  2          0.40              2                 1000     0.21781560    
##   0.010  2          0.40              3                  500     0.20182401    
##   0.010  2          0.40              3                  600     0.20651308    
##   0.010  2          0.40              3                  700     0.20923875    
##   0.010  2          0.40              3                  800     0.21187558    
##   0.010  2          0.40              3                 1000     0.21803993    
##   0.010  2          0.40              4                  500     0.20401280    
##   0.010  2          0.40              4                  600     0.20651308    
##   0.010  2          0.40              4                  700     0.20821258    
##   0.010  2          0.40              4                  800     0.21187586    
##   0.010  2          0.40              4                 1000     0.21727693    
##   0.010  2          0.40              5                  500     0.20267373    
##   0.010  2          0.40              5                  600     0.20611308    
##   0.010  2          0.40              5                  700     0.20852508    
##   0.010  2          0.40              5                  800     0.21111414    
##   0.010  2          0.40              5                 1000     0.21678965    
##   0.010  3          0.20              1                  500     0.20181464    
##   0.010  3          0.20              1                  600     0.20413964    
##   0.010  3          0.20              1                  700     0.20851464    
##   0.010  3          0.20              1                  800     0.21124003    
##   0.010  3          0.20              1                 1000     0.21361454    
##   0.010  3          0.20              2                  500     0.20030214    
##   0.010  3          0.20              2                  600     0.20360281    
##   0.010  3          0.20              2                  700     0.20601503    
##   0.010  3          0.20              2                  800     0.20927697    
##   0.010  3          0.20              2                 1000     0.21214025    
##   0.010  3          0.20              3                  500     0.19998953    
##   0.010  3          0.20              3                  600     0.20351570    
##   0.010  3          0.20              3                  700     0.20570242    
##   0.010  3          0.20              3                  800     0.20936492    
##   0.010  3          0.20              3                 1000     0.21258953    
##   0.010  3          0.20              4                  500     0.20021386    
##   0.010  3          0.20              4                  600     0.20436386    
##   0.010  3          0.20              4                  700     0.20780175    
##   0.010  3          0.20              4                  800     0.20927597    
##   0.010  3          0.20              4                 1000     0.21392664    
##   0.010  3          0.20              5                  500     0.20078914    
##   0.010  3          0.20              5                  600     0.20422742    
##   0.010  3          0.20              5                  700     0.20882831    
##   0.010  3          0.20              5                  800     0.21075231    
##   0.010  3          0.20              5                 1000     0.21495232    
##   0.010  3          0.25              1                  500     0.20570214    
##   0.010  3          0.25              1                  600     0.20874003    
##   0.010  3          0.25              1                  700     0.21200264    
##   0.010  3          0.25              1                  800     0.21330204    
##   0.010  3          0.25              1                 1000     0.21750449    
##   0.010  3          0.25              2                  500     0.20499109    
##   0.010  3          0.25              2                  600     0.20896531    
##   0.010  3          0.25              2                  700     0.21146542    
##   0.010  3          0.25              2                  800     0.21276493    
##   0.010  3          0.25              2                 1000     0.21727821    
##   0.010  3          0.25              3                  500     0.20530214    
##   0.010  3          0.25              3                  600     0.20981542    
##   0.010  3          0.25              3                  700     0.21276493    
##   0.010  3          0.25              3                  800     0.21383954    
##   0.010  3          0.25              3                 1000     0.21781543    
##   0.010  3          0.25              4                  500     0.20476492    
##   0.010  3          0.25              4                  600     0.20851609    
##   0.010  3          0.25              4                  700     0.21146464    
##   0.010  3          0.25              4                  800     0.21276464    
##   0.010  3          0.25              4                 1000     0.21629060    
##   0.010  3          0.25              5                  500     0.20530136    
##   0.010  3          0.25              5                  600     0.20851425    
##   0.010  3          0.25              5                  700     0.21208847    
##   0.010  3          0.25              5                  800     0.21338847    
##   0.010  3          0.25              5                 1000     0.21745282    
##   0.010  3          0.30              1                  500     0.21003925    
##   0.010  3          0.30              1                  600     0.21182608    
##   0.010  3          0.30              1                  700     0.21406397    
##   0.010  3          0.30              1                  800     0.21656465    
##   0.010  3          0.30              1                 1000     0.21991532    
##   0.010  3          0.30              2                  500     0.20825303    
##   0.010  3          0.30              2                  600     0.21111486    
##   0.010  3          0.30              2                  700     0.21281436    
##   0.010  3          0.30              2                  800     0.21522675    
##   0.010  3          0.30              2                 1000     0.21817843    
##   0.010  3          0.30              3                  500     0.20816581    
##   0.010  3          0.30              3                  600     0.21191425    
##   0.010  3          0.30              3                  700     0.21361386    
##   0.010  3          0.30              3                  800     0.21593876    
##   0.010  3          0.30              3                 1000     0.21969082    
##   0.010  3          0.30              4                  500     0.20771464    
##   0.010  3          0.30              4                  600     0.21120225    
##   0.010  3          0.30              4                  700     0.21455254    
##   0.010  3          0.30              4                  800     0.21468954    
##   0.010  3          0.30              4                 1000     0.21844121    
##   0.010  3          0.30              5                  500     0.20972686    
##   0.010  3          0.30              5                  600     0.21258925    
##   0.010  3          0.30              5                  700     0.21361386    
##   0.010  3          0.30              5                  800     0.21696493    
##   0.010  3          0.30              5                 1000     0.21857793    
##   0.010  3          0.35              1                  500     0.21392736    
##   0.010  3          0.35              1                  600     0.21633976    
##   0.010  3          0.35              1                  700     0.21812715    
##   0.010  3          0.35              1                  800     0.21991515    
##   0.010  3          0.35              1                 1000     0.21996582    
##   0.010  3          0.35              2                  500     0.21495069    
##   0.010  3          0.35              2                  600     0.21625019    
##   0.010  3          0.35              2                  700     0.21826504    
##   0.010  3          0.35              2                  800     0.21889004    
##   0.010  3          0.35              2                 1000     0.22027832    
##   0.010  3          0.35              3                  500     0.21588897    
##   0.010  3          0.35              3                  600     0.21553847    
##   0.010  3          0.35              3                  700     0.21866426    
##   0.010  3          0.35              3                  800     0.21951504    
##   0.010  3          0.35              3                 1000     0.22027715    
##   0.010  3          0.35              4                  500     0.21383936    
##   0.010  3          0.35              4                  600     0.21540108    
##   0.010  3          0.35              4                  700     0.21875176    
##   0.010  3          0.35              4                  800     0.21866465    
##   0.010  3          0.35              4                 1000     0.22059043    
##   0.010  3          0.35              5                  500     0.21472765    
##   0.010  3          0.35              5                  600     0.21562687    
##   0.010  3          0.35              5                  700     0.21835176    
##   0.010  3          0.35              5                  800     0.21897832    
##   0.010  3          0.35              5                 1000     0.22099004    
##   0.010  3          0.40              1                  500     0.21548819    
##   0.010  3          0.40              1                  600     0.21705009    
##   0.010  3          0.40              1                  700     0.21843904    
##   0.010  3          0.40              1                  800     0.22013965    
##   0.010  3          0.40              1                 1000     0.22099071    
##   0.010  3          0.40              2                  500     0.21548830    
##   0.010  3          0.40              2                  600     0.21718819    
##   0.010  3          0.40              2                  700     0.21843937    
##   0.010  3          0.40              2                  800     0.21982715    
##   0.010  3          0.40              2                 1000     0.22152754    
##   0.010  3          0.40              3                  500     0.21642580    
##   0.010  3          0.40              3                  600     0.21718780    
##   0.010  3          0.40              3                  700     0.21920254    
##   0.010  3          0.40              3                  800     0.21991465    
##   0.010  3          0.40              3                 1000     0.22144043    
##   0.010  3          0.40              4                  500     0.21562687    
##   0.010  3          0.40              4                  600     0.21732637    
##   0.010  3          0.40              4                  700     0.21889004    
##   0.010  3          0.40              4                  800     0.22014015    
##   0.010  3          0.40              4                 1000     0.22099082    
##   0.010  3          0.40              5                  500     0.21588869    
##   0.010  3          0.40              5                  600     0.21803898    
##   0.010  3          0.40              5                  700     0.21897570    
##   0.010  3          0.40              5                  800     0.22045226    
##   0.010  3          0.40              5                 1000     0.22098926    
##   0.010  4          0.20              1                  500     0.20601425    
##   0.010  4          0.20              1                  600     0.20981570    
##   0.010  4          0.20              1                  700     0.21174003    
##   0.010  4          0.20              1                  800     0.21410175    
##   0.010  4          0.20              1                 1000     0.21629043    
##   0.010  4          0.20              2                  500     0.20459070    
##   0.010  4          0.20              2                  600     0.20981531    
##   0.010  4          0.20              2                  700     0.21213953    
##   0.010  4          0.20              2                  800     0.21410282    
##   0.010  4          0.20              2                 1000     0.21651599    
##   0.010  4          0.20              3                  500     0.20431570    
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##   0.010  4          0.20              3                  700     0.21191571    
##   0.010  4          0.20              3                  800     0.21504110    
##   0.010  4          0.20              3                 1000     0.21651588    
##   0.010  4          0.20              4                  500     0.20316542    
##   0.010  4          0.20              4                  600     0.20816503    
##   0.010  4          0.20              4                  700     0.21134092    
##   0.010  4          0.20              4                  800     0.21304053    
##   0.010  4          0.20              4                 1000     0.21531532    
##   0.010  4          0.20              5                  500     0.20316520    
##   0.010  4          0.20              5                  600     0.20799031    
##   0.010  4          0.20              5                  700     0.21156514    
##   0.010  4          0.20              5                  800     0.21406515    
##   0.010  4          0.20              5                 1000     0.21665271    
##   0.010  4          0.25              1                  500     0.20878953    
##   0.010  4          0.25              1                  600     0.21253875    
##   0.010  4          0.25              1                  700     0.21481415    
##   0.010  4          0.25              1                  800     0.21566454    
##   0.010  4          0.25              1                 1000     0.21910349    
##   0.010  4          0.25              2                  500     0.20990231    
##   0.010  4          0.25              2                  600     0.21365232    
##   0.010  4          0.25              2                  700     0.21504071    
##   0.010  4          0.25              2                  800     0.21722810    
##   0.010  4          0.25              2                 1000     0.21879099    
##   0.010  4          0.25              3                  500     0.21132653    
##   0.010  4          0.25              3                  600     0.21387703    
##   0.010  4          0.25              3                  700     0.21606532    
##   0.010  4          0.25              3                  800     0.21754060    
##   0.010  4          0.25              3                 1000     0.21946627    
##   0.010  4          0.25              4                  500     0.20811531    
##   0.010  4          0.25              4                  600     0.21209031    
##   0.010  4          0.25              4                  700     0.21401453    
##   0.010  4          0.25              4                  800     0.21794071    
##   0.010  4          0.25              4                 1000     0.21924138    
##   0.010  4          0.25              5                  500     0.21070320    
##   0.010  4          0.25              5                  600     0.21360360    
##   0.010  4          0.25              5                  700     0.21575321    
##   0.010  4          0.25              5                  800     0.21660349    
##   0.010  4          0.25              5                 1000     0.21924088    
##   0.010  4          0.30              1                  500     0.21450204    
##   0.010  4          0.30              1                  600     0.21722715    
##   0.010  4          0.30              1                  700     0.21830321    
##   0.010  4          0.30              1                  800     0.21901610    
##   0.010  4          0.30              1                 1000     0.22080321    
##   0.010  4          0.30              2                  500     0.21271464    
##   0.010  4          0.30              2                  600     0.21490176    
##   0.010  4          0.30              2                  700     0.21740265    
##   0.010  4          0.30              2                  800     0.21919082    
##   0.010  4          0.30              2                 1000     0.22049049    
##   0.010  4          0.30              3                  500     0.21186492    
##   0.010  4          0.30              3                  600     0.21544082    
##   0.010  4          0.30              3                  700     0.21660371    
##   0.010  4          0.30              3                  800     0.21892838    
##   0.010  4          0.30              3                 1000     0.22071589    
##   0.010  4          0.30              4                  500     0.21191571    
##   0.010  4          0.30              4                  600     0.21464043    
##   0.010  4          0.30              4                  700     0.21682771    
##   0.010  4          0.30              4                  800     0.21839021    
##   0.010  4          0.30              4                 1000     0.21960299    
##   0.010  4          0.30              5                  500     0.21083992    
##   0.010  4          0.30              5                  600     0.21535321    
##   0.010  4          0.30              5                  700     0.21625449    
##   0.010  4          0.30              5                  800     0.21852927    
##   0.010  4          0.30              5                 1000     0.21969206    
##   0.010  4          0.35              1                  500     0.21767754    
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##   0.010  4          0.35              1                 1000     0.22241561    
##   0.010  4          0.35              2                  500     0.21780254    
##   0.010  4          0.35              2                  600     0.21941454    
##   0.010  4          0.35              2                  700     0.22097743    
##   0.010  4          0.35              2                  800     0.22245321    
##   0.010  4          0.35              2                 1000     0.22187811    
##   0.010  4          0.35              3                  500     0.21651465    
##   0.010  4          0.35              3                  600     0.21955321    
##   0.010  4          0.35              3                  700     0.21977810    
##   0.010  4          0.35              3                  800     0.22174100    
##   0.010  4          0.35              3                 1000     0.22281600    
##   0.010  4          0.35              4                  500     0.21856504    
##   0.010  4          0.35              4                  600     0.21995321    
##   0.010  4          0.35              4                  700     0.22080310    
##   0.010  4          0.35              4                  800     0.22214100    
##   0.010  4          0.35              4                 1000     0.22179100    
##   0.010  4          0.35              5                  500     0.21625293    
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##   0.010  4          0.35              5                  700     0.22071660    
##   0.010  4          0.35              5                  800     0.22227850    
##   0.010  4          0.35              5                 1000     0.22147850    
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##   0.010  4          0.40              1                  700     0.22094021    
##   0.010  4          0.40              1                  800     0.22241561    
##   0.010  4          0.40              1                 1000     0.22317772    
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##   0.010  4          0.40              2                  600     0.22151493    
##   0.010  4          0.40              2                  700     0.22236571    
##   0.010  4          0.40              2                  800     0.22219061    
##   0.010  4          0.40              2                 1000     0.22295321    
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##   0.010  4          0.40              3                  800     0.22219061    
##   0.010  4          0.40              3                 1000     0.22192860    
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##   0.010  4          0.40              4                  600     0.22031560    
##   0.010  4          0.40              4                  700     0.22236561    
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##   0.010  4          0.40              4                 1000     0.22259061    
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##   0.010  4          0.40              5                  700     0.22267782    
##   0.010  4          0.40              5                  800     0.22299061    
##   0.010  4          0.40              5                 1000     0.22357782    
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##   0.010  5          0.20              3                 1000     0.21634099    
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##   0.010  5          0.20              4                  700     0.21227764    
##   0.010  5          0.20              4                  800     0.21406582    
##   0.010  5          0.20              4                 1000     0.21625271    
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##   0.010  5          0.20              5                  600     0.20972586    
##   0.010  5          0.20              5                  700     0.21213847    
##   0.010  5          0.20              5                  800     0.21463936    
##   0.010  5          0.20              5                 1000     0.21687754    
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##   0.010  5          0.25              1                  700     0.21535232    
##   0.010  5          0.25              1                  800     0.21722732    
##   0.010  5          0.25              1                 1000     0.21995310    
##   0.010  5          0.25              2                  500     0.21146521    
##   0.010  5          0.25              2                  600     0.21504071    
##   0.010  5          0.25              2                  700     0.21642771    
##   0.010  5          0.25              2                  800     0.21839099    
##   0.010  5          0.25              2                 1000     0.22031510    
##   0.010  5          0.25              3                  500     0.21035282    
##   0.010  5          0.25              3                  600     0.21330271    
##   0.010  5          0.25              3                  700     0.21588982    
##   0.010  5          0.25              3                  800     0.21714060    
##   0.010  5          0.25              3                 1000     0.21946667    
##   0.010  5          0.25              4                  500     0.21208903    
##   0.010  5          0.25              4                  600     0.21423915    
##   0.010  5          0.25              4                  700     0.21634021    
##   0.010  5          0.25              4                  800     0.21758993    
##   0.010  5          0.25              4                 1000     0.21924088    
##   0.010  5          0.25              5                  500     0.20852781    
##   0.010  5          0.25              5                  600     0.21250310    
##   0.010  5          0.25              5                  700     0.21522821    
##   0.010  5          0.25              5                  800     0.21639099    
##   0.010  5          0.25              5                 1000     0.21772888    
##   0.010  5          0.30              1                  500     0.21490243    
##   0.010  5          0.30              1                  600     0.21606482    
##   0.010  5          0.30              1                  700     0.21669021    
##   0.010  5          0.30              1                  800     0.21941521    
##   0.010  5          0.30              1                 1000     0.22165271    
##   0.010  5          0.30              2                  500     0.21535293    
##   0.010  5          0.30              2                  600     0.21714071    
##   0.010  5          0.30              2                  700     0.21901560    
##   0.010  5          0.30              2                  800     0.21986521    
##   0.010  5          0.30              2                 1000     0.22290389    
##   0.010  5          0.30              3                  500     0.21503943    
##   0.010  5          0.30              3                  600     0.21629099    
##   0.010  5          0.30              3                  700     0.21964088    
##   0.010  5          0.30              3                  800     0.22071667    
##   0.010  5          0.30              3                 1000     0.22227839    
##   0.010  5          0.30              4                  500     0.21464060    
##   0.010  5          0.30              4                  600     0.21682771    
##   0.010  5          0.30              4                  700     0.21785271    
##   0.010  5          0.30              4                  800     0.21790349    
##   0.010  5          0.30              4                 1000     0.21960349    
##   0.010  5          0.30              5                  500     0.21481660    
##   0.010  5          0.30              5                  600     0.21794071    
##   0.010  5          0.30              5                  700     0.21825204    
##   0.010  5          0.30              5                  800     0.21946510    
##   0.010  5          0.30              5                 1000     0.22205299    
##   0.010  5          0.35              1                  500     0.21620310    
##   0.010  5          0.35              1                  600     0.21901521    
##   0.010  5          0.35              1                  700     0.22174100    
##   0.010  5          0.35              1                  800     0.22236561    
##   0.010  5          0.35              1                 1000     0.22304061    
##   0.010  5          0.35              2                  500     0.21964032    
##   0.010  5          0.35              2                  600     0.22026532    
##   0.010  5          0.35              2                  700     0.22187732    
##   0.010  5          0.35              2                  800     0.22343982    
##   0.010  5          0.35              2                 1000     0.22335311    
##   0.010  5          0.35              3                  500     0.21816493    
##   0.010  5          0.35              3                  600     0.21995243    
##   0.010  5          0.35              3                  700     0.22134060    
##   0.010  5          0.35              3                  800     0.22250271    
##   0.010  5          0.35              3                 1000     0.22326482    
##   0.010  5          0.35              4                  500     0.21847771    
##   0.010  5          0.35              4                  600     0.22004021    
##   0.010  5          0.35              4                  700     0.22165271    
##   0.010  5          0.35              4                  800     0.22165271    
##   0.010  5          0.35              4                 1000     0.22343982    
##   0.010  5          0.35              5                  500     0.21839160    
##   0.010  5          0.35              5                  600     0.22017860    
##   0.010  5          0.35              5                  700     0.22165300    
##   0.010  5          0.35              5                  800     0.22219011    
##   0.010  5          0.35              5                 1000     0.22375233    
##   0.010  5          0.40              1                  500     0.22017900    
##   0.010  5          0.40              1                  600     0.22227861    
##   0.010  5          0.40              1                  700     0.22267850    
##   0.010  5          0.40              1                  800     0.22352800    
##   0.010  5          0.40              1                 1000     0.22357811    
##   0.010  5          0.40              2                  500     0.22142771    
##   0.010  5          0.40              2                  600     0.22290232    
##   0.010  5          0.40              2                  700     0.22281522    
##   0.010  5          0.40              2                  800     0.22349061    
##   0.010  5          0.40              2                 1000     0.22389022    
##   0.010  5          0.40              3                  500     0.22009071    
##   0.010  5          0.40              3                  600     0.22125271    
##   0.010  5          0.40              3                  700     0.22210271    
##   0.010  5          0.40              3                  800     0.22232821    
##   0.010  5          0.40              3                 1000     0.22340272    
##   0.010  5          0.40              4                  500     0.21892839    
##   0.010  5          0.40              4                  600     0.22080339    
##   0.010  5          0.40              4                  700     0.22125310    
##   0.010  5          0.40              4                  800     0.22219100    
##   0.010  5          0.40              4                 1000     0.22380233    
##   0.010  5          0.40              5                  500     0.22009099    
##   0.010  5          0.40              5                  600     0.22219089    
##   0.010  5          0.40              5                  700     0.22259050    
##   0.010  5          0.40              5                  800     0.22219061    
##   0.010  5          0.40              5                 1000     0.22406522    
##   0.010  6          0.20              1                  500     0.20418947    
##   0.010  6          0.20              1                  600     0.20776425    
##   0.010  6          0.20              1                  700     0.21173925    
##   0.010  6          0.20              1                  800     0.21316426    
##   0.010  6          0.20              1                 1000     0.21665204    
##   0.010  6          0.20              2                  500     0.20785236    
##   0.010  6          0.20              2                  600     0.21040225    
##   0.010  6          0.20              2                  700     0.21173926    
##   0.010  6          0.20              2                  800     0.21455321    
##   0.010  6          0.20              2                 1000     0.21674071    
##   0.010  6          0.20              3                  500     0.20549003    
##   0.010  6          0.20              3                  600     0.20852714    
##   0.010  6          0.20              3                  700     0.21173993    
##   0.010  6          0.20              3                  800     0.21415293    
##   0.010  6          0.20              3                 1000     0.21696532    
##   0.010  6          0.20              4                  500     0.20593964    
##   0.010  6          0.20              4                  600     0.20946425    
##   0.010  6          0.20              4                  700     0.21165254    
##   0.010  6          0.20              4                  800     0.21491532    
##   0.010  6          0.20              4                 1000     0.21687782    
##   0.010  6          0.20              5                  500     0.20486436    
##   0.010  6          0.20              5                  600     0.20915069    
##   0.010  6          0.20              5                  700     0.21048886    
##   0.010  6          0.20              5                  800     0.21415215    
##   0.010  6          0.20              5                 1000     0.21634043    
##   0.010  6          0.25              1                  500     0.21106476    
##   0.010  6          0.25              1                  600     0.21401582    
##   0.010  6          0.25              1                  700     0.21682832    
##   0.010  6          0.25              1                  800     0.21754043    
##   0.010  6          0.25              1                 1000     0.21870332    
##   0.010  6          0.25              2                  500     0.21182636    
##   0.010  6          0.25              2                  600     0.21321376    
##   0.010  6          0.25              2                  700     0.21625204    
##   0.010  6          0.25              2                  800     0.21687704    
##   0.010  6          0.25              2                 1000     0.21977821    
##   0.010  6          0.25              3                  500     0.21245175    
##   0.010  6          0.25              3                  600     0.21602665    
##   0.010  6          0.25              3                  700     0.21682743    
##   0.010  6          0.25              3                  800     0.21776532    
##   0.010  6          0.25              3                 1000     0.22071571    
##   0.010  6          0.25              4                  500     0.21298993    
##   0.010  6          0.25              4                  600     0.21495204    
##   0.010  6          0.25              4                  700     0.21620282    
##   0.010  6          0.25              4                  800     0.21714121    
##   0.010  6          0.25              4                 1000     0.21889149    
##   0.010  6          0.25              5                  500     0.20941464    
##   0.010  6          0.25              5                  600     0.21446571    
##   0.010  6          0.25              5                  700     0.21647782    
##   0.010  6          0.25              5                  800     0.21781571    
##   0.010  6          0.25              5                 1000     0.22036571    
##   0.010  6          0.30              1                  500     0.21503993    
##   0.010  6          0.30              1                  600     0.21588993    
##   0.010  6          0.30              1                  700     0.21776571    
##   0.010  6          0.30              1                  800     0.21892899    
##   0.010  6          0.30              1                 1000     0.22134138    
##   0.010  6          0.30              2                  500     0.21557665    
##   0.010  6          0.30              2                  600     0.21651454    
##   0.010  6          0.30              2                  700     0.21844032    
##   0.010  6          0.30              2                  800     0.21969071    
##   0.010  6          0.30              2                 1000     0.22054110    
##   0.010  6          0.30              3                  500     0.21464082    
##   0.010  6          0.30              3                  600     0.21714121    
##   0.010  6          0.30              3                  700     0.21861660    
##   0.010  6          0.30              3                  800     0.21986621    
##   0.010  6          0.30              3                 1000     0.21991632    
##   0.010  6          0.30              4                  500     0.21495282    
##   0.010  6          0.30              4                  600     0.21526532    
##   0.010  6          0.30              4                  700     0.21714110    
##   0.010  6          0.30              4                  800     0.21826649    
##   0.010  6          0.30              4                 1000     0.22147860    
##   0.010  6          0.30              5                  500     0.21383993    
##   0.010  6          0.30              5                  600     0.21540321    
##   0.010  6          0.30              5                  700     0.21804149    
##   0.010  6          0.30              5                  800     0.21812849    
##   0.010  6          0.30              5                 1000     0.22125321    
##   0.010  6          0.35              1                  500     0.21807693    
##   0.010  6          0.35              1                  600     0.21995310    
##   0.010  6          0.35              1                  700     0.22182889    
##   0.010  6          0.35              1                  800     0.22384061    
##   0.010  6          0.35              1                 1000     0.22429033    
##   0.010  6          0.35              2                  500     0.21830076    
##   0.010  6          0.35              2                  600     0.22227821    
##   0.010  6          0.35              2                  700     0.22330282    
##   0.010  6          0.35              2                  800     0.22375333    
##   0.010  6          0.35              2                 1000     0.22415322    
##   0.010  6          0.35              3                  500     0.21812821    
##   0.010  6          0.35              3                  600     0.22022849    
##   0.010  6          0.35              3                  700     0.22227761    
##   0.010  6          0.35              3                  800     0.22210310    
##   0.010  6          0.35              3                 1000     0.22429100    
##   0.010  6          0.35              4                  500     0.21986610    
##   0.010  6          0.35              4                  600     0.22049099    
##   0.010  6          0.35              4                  700     0.22134021    
##   0.010  6          0.35              4                  800     0.22241482    
##   0.010  6          0.35              4                 1000     0.22375233    
##   0.010  6          0.35              5                  500     0.21901571    
##   0.010  6          0.35              5                  600     0.21969099    
##   0.010  6          0.35              5                  700     0.22116610    
##   0.010  6          0.35              5                  800     0.22241571    
##   0.010  6          0.35              5                 1000     0.22429072    
##   0.010  6          0.40              1                  500     0.22106532    
##   0.010  6          0.40              1                  600     0.22222771    
##   0.010  6          0.40              1                  700     0.22375282    
##   0.010  6          0.40              1                  800     0.22375282    
##   0.010  6          0.40              1                 1000     0.22406532    
##   0.010  6          0.40              2                  500     0.22182771    
##   0.010  6          0.40              2                  600     0.22290300    
##   0.010  6          0.40              2                  700     0.22352721    
##   0.010  6          0.40              2                  800     0.22303982    
##   0.010  6          0.40              2                 1000     0.22406522    
##   0.010  6          0.40              3                  500     0.21995410    
##   0.010  6          0.40              3                  600     0.22116610    
##   0.010  6          0.40              3                  700     0.22201610    
##   0.010  6          0.40              3                  800     0.22232821    
##   0.010  6          0.40              3                 1000     0.22340243    
##   0.010  6          0.40              4                  500     0.22040399    
##   0.010  6          0.40              4                  600     0.22116699    
##   0.010  6          0.40              4                  700     0.22184110    
##   0.010  6          0.40              4                  800     0.22255321    
##   0.010  6          0.40              4                 1000     0.22309032    
##   0.010  6          0.40              5                  500     0.21977782    
##   0.010  6          0.40              5                  600     0.22156560    
##   0.010  6          0.40              5                  700     0.22227771    
##   0.010  6          0.40              5                  800     0.22375272    
##   0.010  6          0.40              5                 1000     0.22460154    
## 
## Tuning parameter 'gamma' was held constant at a value of 1
## Tuning
##  parameter 'subsample' was held constant at a value of 1
## EconomicProfit was used to select the optimal model using the smallest value.
## The final values used for the model were nrounds = 500, max_depth = 2, eta
##  = 0.001, gamma = 1, colsample_bytree = 0.2, min_child_weight = 5 and
##  subsample = 1.

We see that the best hyper-parameters are: nrounds = 500, eta = 0.001, max_depth = 2, gamma = 1, colsample_bytree = 0.2, min_child_weight = 5 and subsample = 1.

With it, we obtain the predictions and see how well it performs according to our economic profit metric. We now use this model to predict directly using the probability to be able to change the threshold.

threshold = 0.4
xgbProb = predict(xgb.train, newdata=test_data, type="prob")
xgbPred = rep("NOT.QSO", nrow(test_data))
xgbPred[which(xgbProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(xgbPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.1983331

Worse than our previous model, let’s try to improve it looking for the optimal threshold.

profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = best_hyperparameters

j <- 0
for (threshold in seq(0.25, 0.7, 0.05)){
  
  j <- j + 1
  #cat(j)
  for(i in 1:15){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    xgb.train = train(class ~ .,
                  data=train,
                  trControl = ctrl,
                  metric="EconomicProfit",
                  maximize = F,
                  tuneGrid = grid,
                  preProcess = c("center", "scale"),
                  method = "xgbTree"
    )
    
    xgbProb = predict(xgb.train, test, type="prob")
    xgbPred = rep("NOT.QSO", nrow(test))
    xgbPred[which(xgbProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(xgbPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
    
  }
}

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set
## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

## + Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold1: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold2: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold3: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold4: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## + Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## - Fold5: nrounds=500, max_depth=2, eta=0.001, gamma=1, colsample_bytree=0.2, min_child_weight=5, subsample=1 
## Aggregating results
## Fitting final model on full training set

## Warning in confusionMatrix.default(factor(xgbPred), test$class): Levels are not
## in the same order for reference and data. Refactoring data to match.

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq(0.05, 0.5, 0.05), col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] -0.03288870 -0.03288870  0.12817841  0.19877032  0.16936223  0.09845769
##  [7]  0.01031680  0.01031680  0.01031680  0.01031680

Final prediction with the obtained hyper-parameters and optimal threshold.

indexthr = which.max(medians)
threshold = seq(0.25, 0.7, 0.05)[indexthr]
xgbProb = predict(xgb.train, newdata=test_data, type="prob")
xgbPred = rep("NOT.QSO", nrow(test_data))
xgbPred[which(xgbProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(xgbPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.2004501

We still do not have a model better than decision trees, we keep trying with neural networks later.

Variable importance and partial dependencies

We study to see which are the most informative variables.

gb_imp <- varImp(xgb.train, scale = F)
plot(gb_imp, scales = list(y = list(cex = .95)))

redshift is once again the most influential variable.

partial(xgb.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

We obtain the same conclusion as always, but in this one we see that the increase in probability is much more drastic and only until 0.44.

Neural Networks

Neural networks are a class of machine learning models inspired by the structure and functioning of the human brain’s biological neural networks. They consist of interconnected nodes, called neurons or units, organized in layers. Neural networks are capable of learning complex relationships and patterns directly from data, making them powerful tools for various machine learning tasks, including classification, regression, and pattern recognition.

Neural networks can vary significantly in architecture, including the number of layers, the number of neurons in each layer, the type of activation functions used, and the specific training algorithm employed. Neural networks have gained popularity in recent years due to their ability to learn from large, complex datasets and achieve state-of-the-art performance in various domains, including computer vision, natural language processing, and speech recognition. However, training neural networks can be computationally intensive and requires large amounts of data

We use caret to train our model with neural networks and, then, we plot it to observe it.

nn.train <- train(class ~., 
                  method = "nnet", 
                  data = train_data,
                  preProcess = c("center", "scale"),
                  MaxNWts = 1000,
                  maxit = 100,
                  tuneGrid = expand.grid(size=c(2,3,4,5,6), decay=c(0.01,0.007,0.005,0.003,0.001)), 
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)

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## iter  30 value 460.500338
## iter  40 value 376.771069
## iter  50 value 340.760561
## iter  60 value 332.565278
## iter  70 value 319.374754
## iter  80 value 295.871616
## iter  90 value 288.754572
## iter 100 value 285.723729
## final  value 285.723729 
## stopped after 100 iterations
## # weights:  85
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## iter  10 value 606.999038
## iter  20 value 420.975656
## iter  30 value 340.768452
## iter  40 value 276.370944
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## iter  60 value 252.715352
## iter  70 value 247.296982
## iter  80 value 240.893010
## iter  90 value 237.191317
## iter 100 value 236.122303
## final  value 236.122303 
## stopped after 100 iterations
## # weights:  113
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## iter  10 value 364.291290
## iter  20 value 288.622458
## iter  30 value 250.936551
## iter  40 value 220.509779
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## iter  60 value 199.036729
## iter  70 value 194.708816
## iter  80 value 190.902671
## iter  90 value 188.883730
## iter 100 value 186.458136
## final  value 186.458136 
## stopped after 100 iterations
## # weights:  141
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## iter  20 value 257.942092
## iter  30 value 224.734260
## iter  40 value 206.519976
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## iter  70 value 184.027639
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## iter  90 value 180.552301
## iter 100 value 179.496962
## final  value 179.496962 
## stopped after 100 iterations
## # weights:  169
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## iter  30 value 204.863068
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## iter  70 value 160.744201
## iter  80 value 154.753231
## iter  90 value 151.251156
## iter 100 value 147.890613
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## stopped after 100 iterations
## # weights:  57
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## iter  90 value 299.641902
## iter 100 value 298.740044
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## stopped after 100 iterations
## # weights:  85
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## iter  70 value 217.032756
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## stopped after 100 iterations
## # weights:  113
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## iter  30 value 256.591168
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## iter  70 value 203.167648
## iter  80 value 201.900003
## iter  90 value 198.173371
## iter 100 value 196.308885
## final  value 196.308885 
## stopped after 100 iterations
## # weights:  141
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## iter  60 value 211.550098
## iter  70 value 197.414616
## iter  80 value 190.845331
## iter  90 value 186.835130
## iter 100 value 184.355449
## final  value 184.355449 
## stopped after 100 iterations
## # weights:  169
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## iter  10 value 395.993661
## iter  20 value 278.319696
## iter  30 value 231.324681
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## iter  60 value 170.036618
## iter  70 value 163.627578
## iter  80 value 160.244521
## iter  90 value 158.154921
## iter 100 value 156.586922
## final  value 156.586922 
## stopped after 100 iterations
## # weights:  57
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## iter  70 value 311.490504
## iter  80 value 310.436779
## iter  90 value 309.422219
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## stopped after 100 iterations
## # weights:  85
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## iter  70 value 256.688779
## iter  80 value 250.651985
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## stopped after 100 iterations
## # weights:  113
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## iter  80 value 218.298014
## iter  90 value 215.407056
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## stopped after 100 iterations
## # weights:  141
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## stopped after 100 iterations
## # weights:  169
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## iter  90 value 142.604781
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## final  value 139.948524 
## stopped after 100 iterations
## # weights:  57
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## iter 100 value 252.111870
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## stopped after 100 iterations
## # weights:  85
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## iter  10 value 323.947786
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## iter  40 value 237.079567
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## iter  70 value 205.046243
## iter  80 value 201.952297
## iter  90 value 199.013044
## iter 100 value 197.498646
## final  value 197.498646 
## stopped after 100 iterations
## # weights:  113
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## iter  30 value 246.069742
## iter  40 value 212.220915
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## iter  70 value 162.278948
## iter  80 value 155.819333
## iter  90 value 153.149616
## iter 100 value 152.525685
## final  value 152.525685 
## stopped after 100 iterations
## # weights:  141
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## iter  30 value 279.356386
## iter  40 value 245.470173
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## iter  70 value 185.490396
## iter  80 value 177.669339
## iter  90 value 174.577463
## iter 100 value 169.506253
## final  value 169.506253 
## stopped after 100 iterations
## # weights:  169
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## iter  10 value 341.812584
## iter  20 value 251.027658
## iter  30 value 202.212531
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## iter  70 value 148.861927
## iter  80 value 145.691264
## iter  90 value 143.480236
## iter 100 value 142.447418
## final  value 142.447418 
## stopped after 100 iterations
## # weights:  57
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## iter  40 value 337.088211
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## stopped after 100 iterations
## # weights:  85
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## final  value 219.450595 
## stopped after 100 iterations
## # weights:  113
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## iter  80 value 181.570088
## iter  90 value 180.580407
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## final  value 179.365913 
## stopped after 100 iterations
## # weights:  141
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## iter  70 value 174.961544
## iter  80 value 170.925675
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## iter 100 value 164.255602
## final  value 164.255602 
## stopped after 100 iterations
## # weights:  169
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## iter  80 value 132.417379
## iter  90 value 130.256058
## iter 100 value 126.510803
## final  value 126.510803 
## stopped after 100 iterations
## # weights:  57
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## stopped after 100 iterations
## # weights:  85
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## stopped after 100 iterations
## # weights:  113
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## stopped after 100 iterations
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## stopped after 100 iterations
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## stopped after 100 iterations
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## stopped after 100 iterations
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## # weights:  169
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## # weights:  113
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## stopped after 100 iterations
## # weights:  57
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## stopped after 100 iterations
## # weights:  85
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## # weights:  113
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## iter  70 value 193.580483
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## iter  90 value 184.309586
## iter 100 value 181.616964
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## stopped after 100 iterations
## # weights:  141
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## stopped after 100 iterations
## # weights:  169
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## iter  90 value 108.438161
## iter 100 value 106.249486
## final  value 106.249486 
## stopped after 100 iterations
## # weights:  57
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## iter  20 value 508.877383
## iter  30 value 408.381918
## iter  40 value 322.439665
## iter  50 value 293.530773
## iter  60 value 283.616491
## iter  70 value 281.305711
## iter  80 value 278.348050
## iter  90 value 277.408663
## iter 100 value 276.743436
## final  value 276.743436 
## stopped after 100 iterations
## # weights:  85
## initial  value 3165.522137 
## iter  10 value 726.612705
## iter  20 value 425.096621
## iter  30 value 339.229583
## iter  40 value 308.913915
## iter  50 value 278.908243
## iter  60 value 261.053261
## iter  70 value 255.953463
## iter  80 value 253.088976
## iter  90 value 251.968401
## iter 100 value 251.144246
## final  value 251.144246 
## stopped after 100 iterations
## # weights:  113
## initial  value 2065.024489 
## iter  10 value 428.890782
## iter  20 value 316.862378
## iter  30 value 273.679999
## iter  40 value 248.555395
## iter  50 value 236.906863
## iter  60 value 230.908491
## iter  70 value 227.220349
## iter  80 value 226.413406
## iter  90 value 224.149785
## iter 100 value 223.482350
## final  value 223.482350 
## stopped after 100 iterations
## # weights:  141
## initial  value 2354.127243 
## iter  10 value 328.586710
## iter  20 value 247.627423
## iter  30 value 230.421679
## iter  40 value 221.757617
## iter  50 value 209.328517
## iter  60 value 200.289013
## iter  70 value 197.456194
## iter  80 value 196.364439
## iter  90 value 195.624383
## iter 100 value 194.961605
## final  value 194.961605 
## stopped after 100 iterations
## # weights:  169
## initial  value 2491.785810 
## iter  10 value 331.118478
## iter  20 value 243.640865
## iter  30 value 212.709335
## iter  40 value 194.126403
## iter  50 value 179.650012
## iter  60 value 171.605366
## iter  70 value 165.308021
## iter  80 value 159.910930
## iter  90 value 156.327432
## iter 100 value 153.231347
## final  value 153.231347 
## stopped after 100 iterations
## # weights:  57
## initial  value 1809.605653 
## iter  10 value 418.376146
## iter  20 value 342.738409
## iter  30 value 318.434193
## iter  40 value 314.608281
## iter  50 value 313.351299
## iter  60 value 311.661322
## iter  70 value 310.213788
## iter  80 value 309.979872
## iter  90 value 309.773473
## iter 100 value 303.906004
## final  value 303.906004 
## stopped after 100 iterations
## # weights:  85
## initial  value 4181.760856 
## iter  10 value 555.947565
## iter  20 value 399.233018
## iter  30 value 359.412667
## iter  40 value 319.273021
## iter  50 value 296.489662
## iter  60 value 289.586125
## iter  70 value 283.778885
## iter  80 value 280.508972
## iter  90 value 278.254613
## iter 100 value 274.194164
## final  value 274.194164 
## stopped after 100 iterations
## # weights:  113
## initial  value 1617.601633 
## iter  10 value 312.397569
## iter  20 value 271.926177
## iter  30 value 240.118766
## iter  40 value 224.457591
## iter  50 value 213.421747
## iter  60 value 207.635386
## iter  70 value 205.468317
## iter  80 value 203.747255
## iter  90 value 199.248002
## iter 100 value 197.426800
## final  value 197.426800 
## stopped after 100 iterations
## # weights:  141
## initial  value 2147.311514 
## iter  10 value 443.034906
## iter  20 value 316.521420
## iter  30 value 273.141595
## iter  40 value 252.707672
## iter  50 value 230.233231
## iter  60 value 219.175327
## iter  70 value 215.517904
## iter  80 value 213.136918
## iter  90 value 209.837035
## iter 100 value 208.200982
## final  value 208.200982 
## stopped after 100 iterations
## # weights:  169
## initial  value 3129.003012 
## iter  10 value 385.287196
## iter  20 value 314.703797
## iter  30 value 265.351534
## iter  40 value 231.068670
## iter  50 value 207.239422
## iter  60 value 197.943374
## iter  70 value 190.423971
## iter  80 value 184.012393
## iter  90 value 176.980931
## iter 100 value 173.851735
## final  value 173.851735 
## stopped after 100 iterations
## # weights:  57
## initial  value 1643.453741 
## iter  10 value 425.725968
## iter  20 value 347.347752
## iter  30 value 321.319138
## iter  40 value 299.698616
## iter  50 value 286.850069
## iter  60 value 283.346730
## iter  70 value 278.349034
## iter  80 value 274.365399
## iter  90 value 272.484671
## iter 100 value 270.764193
## final  value 270.764193 
## stopped after 100 iterations
## # weights:  85
## initial  value 2433.882190 
## iter  10 value 310.102186
## iter  20 value 267.001976
## iter  30 value 253.370214
## iter  40 value 245.359943
## iter  50 value 239.130475
## iter  60 value 233.296549
## iter  70 value 224.396464
## iter  80 value 218.936888
## iter  90 value 213.821704
## iter 100 value 210.886611
## final  value 210.886611 
## stopped after 100 iterations
## # weights:  113
## initial  value 1764.262645 
## iter  10 value 380.452273
## iter  20 value 303.725358
## iter  30 value 261.281336
## iter  40 value 243.299932
## iter  50 value 235.766779
## iter  60 value 226.489412
## iter  70 value 215.915625
## iter  80 value 207.583963
## iter  90 value 202.754361
## iter 100 value 197.919807
## final  value 197.919807 
## stopped after 100 iterations
## # weights:  141
## initial  value 1935.576620 
## iter  10 value 477.677197
## iter  20 value 355.678682
## iter  30 value 285.593140
## iter  40 value 258.686535
## iter  50 value 244.873952
## iter  60 value 233.372500
## iter  70 value 228.776164
## iter  80 value 223.161577
## iter  90 value 218.748684
## iter 100 value 215.758577
## final  value 215.758577 
## stopped after 100 iterations
## # weights:  169
## initial  value 2236.532223 
## iter  10 value 319.697939
## iter  20 value 257.314110
## iter  30 value 224.253378
## iter  40 value 192.863724
## iter  50 value 172.402993
## iter  60 value 160.459234
## iter  70 value 149.508672
## iter  80 value 140.707939
## iter  90 value 134.996493
## iter 100 value 132.758791
## final  value 132.758791 
## stopped after 100 iterations
## # weights:  57
## initial  value 2582.881346 
## iter  10 value 466.137651
## iter  20 value 381.982017
## iter  30 value 326.506269
## iter  40 value 303.292955
## iter  50 value 284.323616
## iter  60 value 278.559669
## iter  70 value 274.618969
## iter  80 value 272.286505
## iter  90 value 269.375127
## iter 100 value 266.314729
## final  value 266.314729 
## stopped after 100 iterations
## # weights:  85
## initial  value 2258.329974 
## iter  10 value 507.555601
## iter  20 value 378.571185
## iter  30 value 293.470655
## iter  40 value 267.687597
## iter  50 value 256.965029
## iter  60 value 248.886916
## iter  70 value 242.283364
## iter  80 value 240.575563
## iter  90 value 239.188194
## iter 100 value 231.164281
## final  value 231.164281 
## stopped after 100 iterations
## # weights:  113
## initial  value 3885.946751 
## iter  10 value 436.890538
## iter  20 value 306.508229
## iter  30 value 257.896910
## iter  40 value 239.545943
## iter  50 value 229.622434
## iter  60 value 224.317718
## iter  70 value 221.266445
## iter  80 value 218.748979
## iter  90 value 216.577222
## iter 100 value 215.419733
## final  value 215.419733 
## stopped after 100 iterations
## # weights:  141
## initial  value 2008.396543 
## iter  10 value 559.874586
## iter  20 value 368.705947
## iter  30 value 269.452824
## iter  40 value 252.226903
## iter  50 value 243.976099
## iter  60 value 241.159655
## iter  70 value 234.952977
## iter  80 value 226.850098
## iter  90 value 220.342057
## iter 100 value 210.743318
## final  value 210.743318 
## stopped after 100 iterations
## # weights:  169
## initial  value 1957.298750 
## iter  10 value 316.568040
## iter  20 value 251.406163
## iter  30 value 212.063374
## iter  40 value 182.315965
## iter  50 value 159.819277
## iter  60 value 149.857240
## iter  70 value 145.564169
## iter  80 value 141.366636
## iter  90 value 139.279260
## iter 100 value 137.413707
## final  value 137.413707 
## stopped after 100 iterations
## # weights:  57
## initial  value 2841.658456 
## iter  10 value 390.453794
## iter  20 value 331.698094
## iter  30 value 305.916796
## iter  40 value 294.522661
## iter  50 value 285.511430
## iter  60 value 279.070053
## iter  70 value 275.535137
## iter  80 value 274.804064
## iter  90 value 274.721022
## iter 100 value 273.578078
## final  value 273.578078 
## stopped after 100 iterations
## # weights:  85
## initial  value 1848.376613 
## iter  10 value 397.489519
## iter  20 value 305.789095
## iter  30 value 281.361194
## iter  40 value 265.187615
## iter  50 value 250.039694
## iter  60 value 231.554720
## iter  70 value 221.125517
## iter  80 value 218.068988
## iter  90 value 214.500150
## iter 100 value 211.635006
## final  value 211.635006 
## stopped after 100 iterations
## # weights:  113
## initial  value 2340.359954 
## iter  10 value 1102.685154
## iter  20 value 1101.505469
## iter  20 value 1101.505469
## iter  20 value 1101.505468
## final  value 1101.505468 
## converged
## # weights:  141
## initial  value 1852.656461 
## iter  10 value 592.243141
## iter  20 value 309.266100
## iter  30 value 239.413606
## iter  40 value 224.796404
## iter  50 value 210.097737
## iter  60 value 202.079762
## iter  70 value 197.802118
## iter  80 value 194.806434
## iter  90 value 192.803777
## iter 100 value 189.363100
## final  value 189.363100 
## stopped after 100 iterations
## # weights:  169
## initial  value 2080.370897 
## iter  10 value 387.190767
## iter  20 value 261.385987
## iter  30 value 220.265278
## iter  40 value 199.981023
## iter  50 value 185.631005
## iter  60 value 175.953452
## iter  70 value 167.978728
## iter  80 value 162.891676
## iter  90 value 158.272889
## iter 100 value 154.580938
## final  value 154.580938 
## stopped after 100 iterations
## # weights:  57
## initial  value 3059.996952 
## iter  10 value 636.597370
## iter  20 value 401.077584
## iter  30 value 349.536749
## iter  40 value 334.905764
## iter  50 value 326.431237
## iter  60 value 318.532465
## iter  70 value 312.683882
## iter  80 value 302.009215
## iter  90 value 298.837195
## iter 100 value 295.263029
## final  value 295.263029 
## stopped after 100 iterations
## # weights:  85
## initial  value 2626.447212 
## iter  10 value 439.521679
## iter  20 value 309.249823
## iter  30 value 276.542985
## iter  40 value 264.742248
## iter  50 value 249.003372
## iter  60 value 235.304820
## iter  70 value 229.802393
## iter  80 value 223.624806
## iter  90 value 221.018713
## iter 100 value 219.445399
## final  value 219.445399 
## stopped after 100 iterations
## # weights:  113
## initial  value 2153.164765 
## iter  10 value 347.039045
## iter  20 value 261.661070
## iter  30 value 234.822542
## iter  40 value 225.114333
## iter  50 value 220.616030
## iter  60 value 218.639838
## iter  70 value 217.948221
## iter  80 value 215.924601
## iter  90 value 215.462166
## iter 100 value 215.257106
## final  value 215.257106 
## stopped after 100 iterations
## # weights:  141
## initial  value 4405.428963 
## iter  10 value 556.359276
## iter  20 value 406.797423
## iter  30 value 278.145681
## iter  40 value 241.969395
## iter  50 value 230.055709
## iter  60 value 218.820923
## iter  70 value 207.792668
## iter  80 value 200.553447
## iter  90 value 197.427055
## iter 100 value 193.155782
## final  value 193.155782 
## stopped after 100 iterations
## # weights:  169
## initial  value 2656.790685 
## iter  10 value 453.176014
## iter  20 value 290.917374
## iter  30 value 238.199976
## iter  40 value 216.894118
## iter  50 value 208.140553
## iter  60 value 202.039913
## iter  70 value 198.952641
## iter  80 value 196.080931
## iter  90 value 195.244907
## iter 100 value 194.967021
## final  value 194.967021 
## stopped after 100 iterations
## # weights:  57
## initial  value 2112.096189 
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## iter  20 value 603.672276
## iter  30 value 495.904664
## iter  40 value 370.064331
## iter  50 value 317.374287
## iter  60 value 304.372665
## iter  70 value 302.826548
## iter  80 value 301.513560
## iter  90 value 296.715554
## iter 100 value 293.156023
## final  value 293.156023 
## stopped after 100 iterations
## # weights:  85
## initial  value 1725.747850 
## iter  10 value 388.462832
## iter  20 value 303.272058
## iter  30 value 264.181538
## iter  40 value 248.909876
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## iter  60 value 240.608194
## iter  70 value 239.267402
## iter  80 value 236.650050
## iter  90 value 234.500759
## iter 100 value 232.929603
## final  value 232.929603 
## stopped after 100 iterations
## # weights:  113
## initial  value 1948.708300 
## iter  10 value 370.449060
## iter  20 value 268.207653
## iter  30 value 239.965303
## iter  40 value 220.852415
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## iter  60 value 203.685293
## iter  70 value 200.466288
## iter  80 value 196.801841
## iter  90 value 191.753845
## iter 100 value 185.510876
## final  value 185.510876 
## stopped after 100 iterations
## # weights:  141
## initial  value 2351.531791 
## iter  10 value 327.903694
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## iter  30 value 219.731377
## iter  40 value 205.922319
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## iter  60 value 192.282004
## iter  70 value 187.854902
## iter  80 value 186.484793
## iter  90 value 183.743018
## iter 100 value 182.179839
## final  value 182.179839 
## stopped after 100 iterations
## # weights:  169
## initial  value 1940.121432 
## iter  10 value 300.665193
## iter  20 value 227.419787
## iter  30 value 200.635559
## iter  40 value 184.534427
## iter  50 value 170.155280
## iter  60 value 161.863948
## iter  70 value 155.667959
## iter  80 value 152.866644
## iter  90 value 149.953426
## iter 100 value 147.862782
## final  value 147.862782 
## stopped after 100 iterations
## # weights:  57
## initial  value 2343.377252 
## iter  10 value 467.094972
## iter  20 value 363.182373
## iter  30 value 328.274880
## iter  40 value 301.923821
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## iter  60 value 283.809615
## iter  70 value 282.663498
## iter  80 value 281.399341
## iter  90 value 279.993603
## iter 100 value 278.241132
## final  value 278.241132 
## stopped after 100 iterations
## # weights:  85
## initial  value 1845.743394 
## iter  10 value 375.044723
## iter  20 value 286.998369
## iter  30 value 258.327723
## iter  40 value 250.745612
## iter  50 value 247.324203
## iter  60 value 242.530877
## iter  70 value 237.040838
## iter  80 value 230.471297
## iter  90 value 224.442640
## iter 100 value 222.826455
## final  value 222.826455 
## stopped after 100 iterations
## # weights:  113
## initial  value 2469.249795 
## iter  10 value 524.750149
## iter  20 value 329.800621
## iter  30 value 265.722942
## iter  40 value 233.596877
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## iter  60 value 197.642255
## iter  70 value 188.942272
## iter  80 value 184.315161
## iter  90 value 182.985250
## iter 100 value 182.224701
## final  value 182.224701 
## stopped after 100 iterations
## # weights:  141
## initial  value 1994.214301 
## iter  10 value 528.459783
## iter  20 value 293.529562
## iter  30 value 252.570770
## iter  40 value 229.854387
## iter  50 value 222.483159
## iter  60 value 211.606201
## iter  70 value 197.535063
## iter  80 value 188.979134
## iter  90 value 182.173469
## iter 100 value 178.300724
## final  value 178.300724 
## stopped after 100 iterations
## # weights:  169
## initial  value 3453.391437 
## iter  10 value 365.508494
## iter  20 value 266.619410
## iter  30 value 237.251522
## iter  40 value 201.015210
## iter  50 value 177.057778
## iter  60 value 165.744011
## iter  70 value 160.087174
## iter  80 value 155.721267
## iter  90 value 152.377456
## iter 100 value 151.164180
## final  value 151.164180 
## stopped after 100 iterations
## # weights:  57
## initial  value 1628.141056 
## iter  10 value 609.328412
## iter  20 value 446.857466
## iter  30 value 366.023711
## iter  40 value 346.107393
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## iter  60 value 330.539005
## iter  70 value 323.409931
## iter  80 value 320.176139
## iter  90 value 316.141349
## iter 100 value 309.545565
## final  value 309.545565 
## stopped after 100 iterations
## # weights:  85
## initial  value 1858.828773 
## iter  10 value 329.764828
## iter  20 value 273.162730
## iter  30 value 248.464611
## iter  40 value 232.594483
## iter  50 value 225.488060
## iter  60 value 222.909734
## iter  70 value 220.694882
## iter  80 value 215.804566
## iter  90 value 213.209281
## iter 100 value 211.542397
## final  value 211.542397 
## stopped after 100 iterations
## # weights:  113
## initial  value 2798.622103 
## iter  10 value 326.189244
## iter  20 value 282.943833
## iter  30 value 257.069342
## iter  40 value 235.827063
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## iter  60 value 196.556491
## iter  70 value 190.693064
## iter  80 value 188.117367
## iter  90 value 186.883013
## iter 100 value 185.700267
## final  value 185.700267 
## stopped after 100 iterations
## # weights:  141
## initial  value 3044.100467 
## iter  10 value 271.405318
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## iter  30 value 223.719206
## iter  40 value 210.075635
## iter  50 value 194.579497
## iter  60 value 182.023746
## iter  70 value 172.528042
## iter  80 value 167.856064
## iter  90 value 164.842956
## iter 100 value 162.940981
## final  value 162.940981 
## stopped after 100 iterations
## # weights:  169
## initial  value 1864.529947 
## iter  10 value 324.548088
## iter  20 value 232.639345
## iter  30 value 194.227360
## iter  40 value 174.986520
## iter  50 value 161.664097
## iter  60 value 153.481070
## iter  70 value 147.478085
## iter  80 value 144.775444
## iter  90 value 143.284812
## iter 100 value 141.857921
## final  value 141.857921 
## stopped after 100 iterations
## # weights:  57
## initial  value 2587.131307 
## iter  10 value 533.020794
## iter  20 value 429.841754
## iter  30 value 371.093297
## iter  40 value 338.978949
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## iter  60 value 309.694796
## iter  70 value 304.261814
## iter  80 value 297.912480
## iter  90 value 287.693855
## iter 100 value 286.634989
## final  value 286.634989 
## stopped after 100 iterations
## # weights:  85
## initial  value 1846.552384 
## iter  10 value 403.566913
## iter  20 value 323.180257
## iter  30 value 289.464571
## iter  40 value 269.653981
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## iter  60 value 255.963695
## iter  70 value 248.074214
## iter  80 value 244.980502
## iter  90 value 242.937936
## iter 100 value 234.183767
## final  value 234.183767 
## stopped after 100 iterations
## # weights:  113
## initial  value 1659.524727 
## iter  10 value 403.819888
## iter  20 value 269.449407
## iter  30 value 219.868286
## iter  40 value 198.866790
## iter  50 value 191.673292
## iter  60 value 184.140677
## iter  70 value 177.524344
## iter  80 value 174.053146
## iter  90 value 171.972321
## iter 100 value 170.256192
## final  value 170.256192 
## stopped after 100 iterations
## # weights:  141
## initial  value 1486.933730 
## iter  10 value 290.806715
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## iter  30 value 207.637943
## iter  40 value 193.901044
## iter  50 value 183.692423
## iter  60 value 176.346413
## iter  70 value 170.960150
## iter  80 value 168.357812
## iter  90 value 166.799875
## iter 100 value 164.366850
## final  value 164.366850 
## stopped after 100 iterations
## # weights:  169
## initial  value 1723.724216 
## iter  10 value 326.690419
## iter  20 value 252.604249
## iter  30 value 224.153930
## iter  40 value 204.770984
## iter  50 value 191.211518
## iter  60 value 181.586286
## iter  70 value 173.790693
## iter  80 value 166.968632
## iter  90 value 164.036482
## iter 100 value 162.208720
## final  value 162.208720 
## stopped after 100 iterations
## # weights:  57
## initial  value 1937.216609 
## iter  10 value 414.604138
## iter  20 value 339.054867
## iter  30 value 304.800254
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## iter  50 value 284.129718
## iter  60 value 282.022154
## iter  70 value 276.715076
## iter  80 value 276.548080
## iter  90 value 276.476328
## iter 100 value 276.467748
## final  value 276.467748 
## stopped after 100 iterations
## # weights:  85
## initial  value 2388.643687 
## iter  10 value 421.968562
## iter  20 value 308.903212
## iter  30 value 280.114997
## iter  40 value 268.276942
## iter  50 value 262.543207
## iter  60 value 256.818136
## iter  70 value 251.229554
## iter  80 value 246.519083
## iter  90 value 243.061952
## iter 100 value 236.001436
## final  value 236.001436 
## stopped after 100 iterations
## # weights:  113
## initial  value 1675.865114 
## iter  10 value 494.614269
## iter  20 value 332.389151
## iter  30 value 291.428385
## iter  40 value 254.837266
## iter  50 value 234.174023
## iter  60 value 213.394804
## iter  70 value 203.394134
## iter  80 value 201.303747
## iter  90 value 199.159781
## iter 100 value 195.260298
## final  value 195.260298 
## stopped after 100 iterations
## # weights:  141
## initial  value 1737.438000 
## iter  10 value 345.181688
## iter  20 value 296.491821
## iter  30 value 263.771150
## iter  40 value 227.397239
## iter  50 value 212.220983
## iter  60 value 202.650888
## iter  70 value 195.807117
## iter  80 value 190.874298
## iter  90 value 188.265318
## iter 100 value 186.430116
## final  value 186.430116 
## stopped after 100 iterations
## # weights:  169
## initial  value 2521.350953 
## iter  10 value 460.597719
## iter  20 value 269.692062
## iter  30 value 232.782609
## iter  40 value 211.312176
## iter  50 value 197.311147
## iter  60 value 190.319904
## iter  70 value 184.036897
## iter  80 value 182.579201
## iter  90 value 180.938991
## iter 100 value 179.744064
## final  value 179.744064 
## stopped after 100 iterations
## # weights:  57
## initial  value 2056.079700 
## iter  10 value 463.948172
## iter  20 value 358.298494
## iter  30 value 333.931866
## iter  40 value 320.742720
## iter  50 value 305.029372
## iter  60 value 291.598039
## iter  70 value 287.086985
## iter  80 value 284.972063
## iter  90 value 281.916076
## iter 100 value 274.319193
## final  value 274.319193 
## stopped after 100 iterations
## # weights:  85
## initial  value 2355.626468 
## iter  10 value 362.602478
## iter  20 value 296.131601
## iter  30 value 253.935650
## iter  40 value 235.963005
## iter  50 value 226.830392
## iter  60 value 224.691478
## iter  70 value 222.369289
## iter  80 value 221.017967
## iter  90 value 219.252459
## iter 100 value 218.342862
## final  value 218.342862 
## stopped after 100 iterations
## # weights:  113
## initial  value 2313.978385 
## iter  10 value 312.843622
## iter  20 value 259.912841
## iter  30 value 216.071948
## iter  40 value 191.158486
## iter  50 value 183.042587
## iter  60 value 175.761621
## iter  70 value 169.855014
## iter  80 value 166.051261
## iter  90 value 164.528586
## iter 100 value 163.898970
## final  value 163.898970 
## stopped after 100 iterations
## # weights:  141
## initial  value 1836.384506 
## iter  10 value 457.741496
## iter  20 value 344.113192
## iter  30 value 256.904950
## iter  40 value 219.976656
## iter  50 value 198.881263
## iter  60 value 189.003170
## iter  70 value 182.256673
## iter  80 value 178.049287
## iter  90 value 176.268918
## iter 100 value 175.586705
## final  value 175.586705 
## stopped after 100 iterations
## # weights:  169
## initial  value 2205.662528 
## iter  10 value 342.067438
## iter  20 value 239.801260
## iter  30 value 203.525476
## iter  40 value 188.008039
## iter  50 value 175.793515
## iter  60 value 171.017605
## iter  70 value 166.991654
## iter  80 value 163.562028
## iter  90 value 162.227035
## iter 100 value 161.441042
## final  value 161.441042 
## stopped after 100 iterations
## # weights:  57
## initial  value 3193.648217 
## iter  10 value 503.686798
## iter  20 value 408.013332
## iter  30 value 361.913837
## iter  40 value 330.283835
## iter  50 value 316.568160
## iter  60 value 305.567988
## iter  70 value 294.752584
## iter  80 value 291.852385
## iter  90 value 287.591228
## iter 100 value 287.090501
## final  value 287.090501 
## stopped after 100 iterations
## # weights:  85
## initial  value 2507.316969 
## iter  10 value 794.045269
## iter  20 value 433.069328
## iter  30 value 308.454999
## iter  40 value 269.226385
## iter  50 value 257.571888
## iter  60 value 251.985931
## iter  70 value 246.654457
## iter  80 value 241.762504
## iter  90 value 239.074521
## iter 100 value 237.895893
## final  value 237.895893 
## stopped after 100 iterations
## # weights:  113
## initial  value 2085.710607 
## iter  10 value 539.898538
## iter  20 value 331.007938
## iter  30 value 286.005004
## iter  40 value 256.271012
## iter  50 value 244.882989
## iter  60 value 236.378509
## iter  70 value 229.088585
## iter  80 value 227.489404
## iter  90 value 226.469933
## iter 100 value 226.070548
## final  value 226.070548 
## stopped after 100 iterations
## # weights:  141
## initial  value 2257.289524 
## iter  10 value 320.974017
## iter  20 value 261.282375
## iter  30 value 217.718637
## iter  40 value 196.835151
## iter  50 value 188.198825
## iter  60 value 184.041738
## iter  70 value 181.919998
## iter  80 value 177.900103
## iter  90 value 171.827157
## iter 100 value 168.471008
## final  value 168.471008 
## stopped after 100 iterations
## # weights:  169
## initial  value 3013.887084 
## iter  10 value 356.677145
## iter  20 value 233.909949
## iter  30 value 203.331199
## iter  40 value 187.622041
## iter  50 value 171.155617
## iter  60 value 162.959479
## iter  70 value 160.417814
## iter  80 value 159.127049
## iter  90 value 153.220577
## iter 100 value 152.360201
## final  value 152.360201 
## stopped after 100 iterations
## # weights:  57
## initial  value 2365.866608 
## iter  10 value 439.575530
## iter  20 value 375.432139
## iter  30 value 346.360114
## iter  40 value 312.060942
## iter  50 value 292.659821
## iter  60 value 274.287150
## iter  70 value 263.401757
## iter  80 value 254.852104
## iter  90 value 254.174514
## iter 100 value 254.131284
## final  value 254.131284 
## stopped after 100 iterations
## # weights:  85
## initial  value 2334.317222 
## iter  10 value 445.502560
## iter  20 value 354.551858
## iter  30 value 312.538542
## iter  40 value 272.480627
## iter  50 value 250.465116
## iter  60 value 247.022416
## iter  70 value 246.358908
## iter  80 value 245.204170
## iter  90 value 241.094574
## iter 100 value 236.024821
## final  value 236.024821 
## stopped after 100 iterations
## # weights:  113
## initial  value 2108.368698 
## iter  10 value 577.841776
## iter  20 value 288.741560
## iter  30 value 260.079594
## iter  40 value 244.707053
## iter  50 value 228.521573
## iter  60 value 220.119161
## iter  70 value 216.465640
## iter  80 value 214.398299
## iter  90 value 213.338546
## iter 100 value 212.179214
## final  value 212.179214 
## stopped after 100 iterations
## # weights:  141
## initial  value 2687.931106 
## iter  10 value 396.760864
## iter  20 value 252.074482
## iter  30 value 213.923209
## iter  40 value 194.649353
## iter  50 value 185.613541
## iter  60 value 178.685487
## iter  70 value 171.994808
## iter  80 value 165.399428
## iter  90 value 161.910739
## iter 100 value 159.804210
## final  value 159.804210 
## stopped after 100 iterations
## # weights:  169
## initial  value 2016.078358 
## iter  10 value 304.336842
## iter  20 value 221.410662
## iter  30 value 191.043134
## iter  40 value 168.007400
## iter  50 value 151.762952
## iter  60 value 144.792765
## iter  70 value 140.114116
## iter  80 value 138.347275
## iter  90 value 137.181445
## iter 100 value 135.921327
## final  value 135.921327 
## stopped after 100 iterations
## # weights:  57
## initial  value 3413.668584 
## iter  10 value 690.060682
## iter  20 value 558.636994
## iter  30 value 485.287557
## iter  40 value 399.697105
## iter  50 value 330.988537
## iter  60 value 318.763989
## iter  70 value 310.932790
## iter  80 value 307.776798
## iter  90 value 303.748355
## iter 100 value 303.208377
## final  value 303.208377 
## stopped after 100 iterations
## # weights:  85
## initial  value 3083.132601 
## iter  10 value 648.181288
## iter  20 value 410.874044
## iter  30 value 303.793190
## iter  40 value 272.152015
## iter  50 value 247.960900
## iter  60 value 239.116909
## iter  70 value 232.401464
## iter  80 value 229.521068
## iter  90 value 227.075234
## iter 100 value 223.543550
## final  value 223.543550 
## stopped after 100 iterations
## # weights:  113
## initial  value 2402.592048 
## iter  10 value 344.291626
## iter  20 value 283.763994
## iter  30 value 248.621807
## iter  40 value 234.320172
## iter  50 value 223.146158
## iter  60 value 218.227182
## iter  70 value 214.682562
## iter  80 value 211.042884
## iter  90 value 206.951760
## iter 100 value 203.077237
## final  value 203.077237 
## stopped after 100 iterations
## # weights:  141
## initial  value 2075.182355 
## iter  10 value 306.168736
## iter  20 value 222.453516
## iter  30 value 200.518323
## iter  40 value 188.454875
## iter  50 value 174.995344
## iter  60 value 161.870997
## iter  70 value 156.489300
## iter  80 value 154.028189
## iter  90 value 151.625361
## iter 100 value 148.896546
## final  value 148.896546 
## stopped after 100 iterations
## # weights:  169
## initial  value 1718.848008 
## iter  10 value 342.685583
## iter  20 value 238.582027
## iter  30 value 193.236624
## iter  40 value 164.843919
## iter  50 value 139.884636
## iter  60 value 124.214240
## iter  70 value 112.145100
## iter  80 value 107.268129
## iter  90 value 104.034272
## iter 100 value 102.516430
## final  value 102.516430 
## stopped after 100 iterations
## # weights:  169
## initial  value 2736.103576 
## iter  10 value 783.534505
## iter  20 value 518.145396
## iter  30 value 364.521208
## iter  40 value 311.688235
## iter  50 value 291.156454
## iter  60 value 272.821528
## iter  70 value 261.542331
## iter  80 value 257.345797
## iter  90 value 254.332942
## iter 100 value 251.173859
## final  value 251.173859 
## stopped after 100 iterations

plot(nn.train)

best_hyperparameters = nn.train$bestTune

We see that the best hyper-parameter for this model are size = 6 and decay = 0.001. We use our model that has been trained with these hyper-parameters, to predict out test dataset. We try changing the threshold manually to see if we can improve the prediction, as we did in the previous.

threshold = 0.2
nnProb = predict(nn.train, newdata=test_data, type="prob")
nnPred = rep("NOT.QSO", nrow(test_data))
nnPred[which(nnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(nnPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.215961

Not a bad profit 0.215961, but still not best that the decision trees one. Furthermore, we do not know if this ithreshold s the best one or not, so we compute the optimal threshold as in the previous ones.

profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = best_hyperparameters

j <- 0
for (threshold in seq(0.05, 0.5, 0.05)){
  
  j <- j + 1
  #cat(j)
  for(i in 1:15){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    

    nn.train <- train(class ~., 
                  method = "nnet", 
                  data = train,
                  preProcess = c("center", "scale"),
                  MaxNWts = 1000,
                  maxit = 100,
                  tuneGrid = grid, 
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)
    
    nnProb = predict(nn.train, test, type="prob")
    nnPred = rep("NOT.QSO", nrow(test))
    nnPred[which(nnProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(nnPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
    
  }
}

## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 945.321781 
## iter  10 value 96.950654
## iter  20 value 60.645659
## iter  30 value 49.548939
## iter  40 value 44.612871
## iter  50 value 41.606362
## iter  60 value 38.463452
## iter  70 value 37.266314
## iter  80 value 36.848567
## iter  90 value 34.427432
## iter 100 value 33.770300
## final  value 33.770300 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1760.103598 
## iter  10 value 107.984121
## iter  20 value 61.780408
## iter  30 value 36.294819
## iter  40 value 30.481176
## iter  50 value 28.652083
## iter  60 value 25.264643
## iter  70 value 22.359722
## iter  80 value 19.755607
## iter  90 value 18.269057
## iter 100 value 17.102593
## final  value 17.102593 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1017.074802 
## iter  10 value 97.891651
## iter  20 value 56.244287
## iter  30 value 41.679764
## iter  40 value 36.028021
## iter  50 value 29.844150
## iter  60 value 27.482600
## iter  70 value 26.667249
## iter  80 value 24.434679
## iter  90 value 23.974440
## iter 100 value 23.084343
## final  value 23.084343 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1047.173722 
## iter  10 value 107.403321
## iter  20 value 57.866114
## iter  30 value 38.362331
## iter  40 value 33.189471
## iter  50 value 32.060109
## iter  60 value 31.222758
## iter  70 value 30.055332
## iter  80 value 29.367803
## iter  90 value 28.171972
## iter 100 value 27.459463
## final  value 27.459463 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1275.252336 
## iter  10 value 111.589459
## iter  20 value 75.109715
## iter  30 value 57.117149
## iter  40 value 42.081503
## iter  50 value 37.192711
## iter  60 value 35.628121
## iter  70 value 34.781895
## iter  80 value 34.279869
## iter  90 value 33.916271
## iter 100 value 33.704603
## final  value 33.704603 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1583.476666 
## iter  10 value 164.304423
## iter  20 value 87.909596
## iter  30 value 57.018061
## iter  40 value 47.668804
## iter  50 value 40.398890
## iter  60 value 39.275685
## iter  70 value 38.372810
## iter  80 value 37.993569
## iter  90 value 37.642275
## iter 100 value 37.430802
## final  value 37.430802 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 850.903836 
## iter  10 value 110.802175
## iter  20 value 81.159590
## iter  30 value 48.585397
## iter  40 value 30.543666
## iter  50 value 26.196909
## iter  60 value 24.682898
## iter  70 value 23.905446
## iter  80 value 23.409151
## iter  90 value 23.131656
## iter 100 value 22.753765
## final  value 22.753765 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 729.281609 
## iter  10 value 156.325436
## iter  20 value 80.301493
## iter  30 value 45.680059
## iter  40 value 34.067559
## iter  50 value 25.769247
## iter  60 value 22.839934
## iter  70 value 21.063898
## iter  80 value 20.240371
## iter  90 value 19.812821
## iter 100 value 19.511026
## final  value 19.511026 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 724.617370 
## iter  10 value 103.521686
## iter  20 value 65.136906
## iter  30 value 49.053640
## iter  40 value 41.720926
## iter  50 value 37.539571
## iter  60 value 35.820666
## iter  70 value 34.643130
## iter  80 value 33.691922
## iter  90 value 33.366618
## iter 100 value 32.182917
## final  value 32.182917 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 893.748246 
## iter  10 value 81.312273
## iter  20 value 53.060974
## iter  30 value 35.698359
## iter  40 value 30.598553
## iter  50 value 26.470484
## iter  60 value 24.856827
## iter  70 value 23.965550
## iter  80 value 22.956973
## iter  90 value 22.505379
## iter 100 value 21.740434
## final  value 21.740434 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1344.133430 
## iter  10 value 118.591252
## iter  20 value 70.981706
## iter  30 value 44.348862
## iter  40 value 31.051318
## iter  50 value 22.494728
## iter  60 value 18.211471
## iter  70 value 16.591179
## iter  80 value 15.489150
## iter  90 value 14.794303
## iter 100 value 14.550403
## final  value 14.550403 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 916.344782 
## iter  10 value 127.773356
## iter  20 value 72.073798
## iter  30 value 48.704666
## iter  40 value 35.300776
## iter  50 value 29.502184
## iter  60 value 26.473324
## iter  70 value 25.402802
## iter  80 value 24.880071
## iter  90 value 24.560526
## iter 100 value 24.254785
## final  value 24.254785 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 726.532063 
## iter  10 value 108.422287
## iter  20 value 59.336032
## iter  30 value 32.927780
## iter  40 value 28.486629
## iter  50 value 26.021534
## iter  60 value 22.691711
## iter  70 value 21.709835
## iter  80 value 20.418472
## iter  90 value 19.722035
## iter 100 value 19.194119
## final  value 19.194119 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1202.186691 
## iter  10 value 134.077038
## iter  20 value 70.050700
## iter  30 value 45.354261
## iter  40 value 38.071880
## iter  50 value 36.261740
## iter  60 value 35.436318
## iter  70 value 33.210736
## iter  80 value 32.539654
## iter  90 value 32.017328
## iter 100 value 31.501918
## final  value 31.501918 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 719.095959 
## iter  10 value 121.874593
## iter  20 value 66.248922
## iter  30 value 42.497828
## iter  40 value 27.795275
## iter  50 value 20.212947
## iter  60 value 17.651156
## iter  70 value 14.467887
## iter  80 value 12.493363
## iter  90 value 10.740396
## iter 100 value 9.705110
## final  value 9.705110 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1096.312949 
## iter  10 value 109.842755
## iter  20 value 63.228603
## iter  30 value 44.965983
## iter  40 value 27.984692
## iter  50 value 20.446684
## iter  60 value 17.541095
## iter  70 value 15.597223
## iter  80 value 14.607388
## iter  90 value 13.360680
## iter 100 value 12.710037
## final  value 12.710037 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 986.204221 
## iter  10 value 112.519337
## iter  20 value 71.240579
## iter  30 value 54.242144
## iter  40 value 41.865712
## iter  50 value 35.098090
## iter  60 value 31.938105
## iter  70 value 29.845867
## iter  80 value 24.960941
## iter  90 value 21.740588
## iter 100 value 21.083500
## final  value 21.083500 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1510.186610 
## iter  10 value 193.080477
## iter  20 value 117.295007
## iter  30 value 92.982511
## iter  40 value 75.133095
## iter  50 value 62.682741
## iter  60 value 53.445209
## iter  70 value 49.681259
## iter  80 value 47.661991
## iter  90 value 46.782790
## iter 100 value 43.758140
## final  value 43.758140 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1804.349462 
## iter  10 value 137.133047
## iter  20 value 94.386570
## iter  30 value 70.836058
## iter  40 value 55.381618
## iter  50 value 49.770167
## iter  60 value 45.914020
## iter  70 value 44.811140
## iter  80 value 40.995810
## iter  90 value 38.572685
## iter 100 value 36.946763
## final  value 36.946763 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 904.998518 
## iter  10 value 130.250358
## iter  20 value 85.835158
## iter  30 value 44.905199
## iter  40 value 28.140874
## iter  50 value 20.696490
## iter  60 value 18.801289
## iter  70 value 17.838140
## iter  80 value 16.288881
## iter  90 value 14.674613
## iter 100 value 13.701506
## final  value 13.701506 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1525.271431 
## iter  10 value 187.199784
## iter  20 value 92.664257
## iter  30 value 58.998296
## iter  40 value 38.031952
## iter  50 value 30.333704
## iter  60 value 27.866310
## iter  70 value 25.876959
## iter  80 value 25.020377
## iter  90 value 24.397784
## iter 100 value 24.219800
## final  value 24.219800 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 766.117880 
## iter  10 value 92.643336
## iter  20 value 61.207067
## iter  30 value 33.771475
## iter  40 value 19.731389
## iter  50 value 15.828832
## iter  60 value 14.989753
## iter  70 value 14.170468
## iter  80 value 12.261019
## iter  90 value 10.029219
## iter 100 value 9.461530
## final  value 9.461530 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1265.069839 
## iter  10 value 134.101554
## iter  20 value 85.549343
## iter  30 value 69.484584
## iter  40 value 58.907689
## iter  50 value 55.371988
## iter  60 value 53.100256
## iter  70 value 50.680216
## iter  80 value 49.759944
## iter  90 value 48.304137
## iter 100 value 47.612027
## final  value 47.612027 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1065.324186 
## iter  10 value 168.405600
## iter  20 value 118.425815
## iter  30 value 77.022857
## iter  40 value 48.959538
## iter  50 value 26.857310
## iter  60 value 19.694880
## iter  70 value 18.435568
## iter  80 value 16.990796
## iter  90 value 16.129934
## iter 100 value 15.314269
## final  value 15.314269 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1063.930200 
## iter  10 value 90.277361
## iter  20 value 53.635758
## iter  30 value 34.784506
## iter  40 value 24.867058
## iter  50 value 18.548377
## iter  60 value 14.582522
## iter  70 value 12.743963
## iter  80 value 11.092757
## iter  90 value 9.771132
## iter 100 value 8.949954
## final  value 8.949954 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1543.278680 
## iter  10 value 108.932178
## iter  20 value 67.037740
## iter  30 value 48.215751
## iter  40 value 38.403620
## iter  50 value 32.473581
## iter  60 value 30.387143
## iter  70 value 28.116358
## iter  80 value 26.881915
## iter  90 value 25.597112
## iter 100 value 24.168051
## final  value 24.168051 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 939.409660 
## iter  10 value 179.226084
## iter  20 value 81.606853
## iter  30 value 40.432177
## iter  40 value 22.242051
## iter  50 value 17.924768
## iter  60 value 16.525211
## iter  70 value 15.921796
## iter  80 value 15.536936
## iter  90 value 14.408779
## iter 100 value 9.081317
## final  value 9.081317 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1231.729750 
## iter  10 value 113.143043
## iter  20 value 66.121779
## iter  30 value 34.260814
## iter  40 value 20.501191
## iter  50 value 17.169730
## iter  60 value 15.032130
## iter  70 value 13.796890
## iter  80 value 13.258160
## iter  90 value 12.436665
## iter 100 value 11.861248
## final  value 11.861248 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 621.111229 
## iter  10 value 166.868808
## iter  20 value 81.426101
## iter  30 value 52.285651
## iter  40 value 43.316417
## iter  50 value 37.991377
## iter  60 value 33.728667
## iter  70 value 27.326256
## iter  80 value 25.172212
## iter  90 value 23.197060
## iter 100 value 21.590129
## final  value 21.590129 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1488.041994 
## iter  10 value 147.067450
## iter  20 value 104.413688
## iter  30 value 79.160972
## iter  40 value 64.945575
## iter  50 value 54.898965
## iter  60 value 47.593490
## iter  70 value 46.194445
## iter  80 value 45.092629
## iter  90 value 39.697478
## iter 100 value 37.609373
## final  value 37.609373 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1347.696008 
## iter  10 value 144.091876
## iter  20 value 67.071711
## iter  30 value 46.027909
## iter  40 value 35.170743
## iter  50 value 31.611916
## iter  60 value 28.432311
## iter  70 value 27.888557
## iter  80 value 27.132675
## iter  90 value 26.752185
## iter 100 value 26.393955
## final  value 26.393955 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1547.384140 
## iter  10 value 105.424740
## iter  20 value 58.034810
## iter  30 value 41.867642
## iter  40 value 32.913488
## iter  50 value 27.481197
## iter  60 value 24.915112
## iter  70 value 23.275126
## iter  80 value 22.039494
## iter  90 value 20.766077
## iter 100 value 19.618121
## final  value 19.618121 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 795.292653 
## iter  10 value 108.586070
## iter  20 value 65.059477
## iter  30 value 41.760316
## iter  40 value 20.851112
## iter  50 value 15.987781
## iter  60 value 14.351687
## iter  70 value 12.270459
## iter  80 value 11.462916
## iter  90 value 11.168573
## iter 100 value 11.003173
## final  value 11.003173 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1082.469459 
## iter  10 value 117.465930
## iter  20 value 70.668365
## iter  30 value 60.989250
## iter  40 value 56.023674
## iter  50 value 53.639648
## iter  60 value 47.611157
## iter  70 value 44.281986
## iter  80 value 39.265653
## iter  90 value 37.664477
## iter 100 value 35.911993
## final  value 35.911993 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1046.565457 
## iter  10 value 126.650826
## iter  20 value 87.699296
## iter  30 value 56.042569
## iter  40 value 39.658634
## iter  50 value 34.182279
## iter  60 value 32.081485
## iter  70 value 28.915759
## iter  80 value 24.285200
## iter  90 value 22.814895
## iter 100 value 21.610969
## final  value 21.610969 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1015.745777 
## iter  10 value 179.868666
## iter  20 value 104.813698
## iter  30 value 74.216496
## iter  40 value 57.935359
## iter  50 value 49.655381
## iter  60 value 45.256037
## iter  70 value 42.406636
## iter  80 value 40.462237
## iter  90 value 39.305007
## iter 100 value 38.182957
## final  value 38.182957 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 864.499296 
## iter  10 value 109.342514
## iter  20 value 62.853219
## iter  30 value 34.127291
## iter  40 value 21.435639
## iter  50 value 19.329505
## iter  60 value 17.685764
## iter  70 value 16.108956
## iter  80 value 14.646257
## iter  90 value 14.017792
## iter 100 value 13.213192
## final  value 13.213192 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 990.600178 
## iter  10 value 105.129863
## iter  20 value 65.487473
## iter  30 value 35.315317
## iter  40 value 29.035759
## iter  50 value 26.398770
## iter  60 value 22.975013
## iter  70 value 20.736862
## iter  80 value 19.106579
## iter  90 value 15.963698
## iter 100 value 14.724686
## final  value 14.724686 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1263.295810 
## iter  10 value 94.722050
## iter  20 value 61.298761
## iter  30 value 41.848763
## iter  40 value 37.123515
## iter  50 value 35.880503
## iter  60 value 34.307144
## iter  70 value 28.813728
## iter  80 value 26.789888
## iter  90 value 21.695835
## iter 100 value 19.765778
## final  value 19.765778 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 945.075385 
## iter  10 value 122.341028
## iter  20 value 69.242092
## iter  30 value 38.549904
## iter  40 value 25.295318
## iter  50 value 21.417658
## iter  60 value 18.787293
## iter  70 value 17.696850
## iter  80 value 16.907467
## iter  90 value 15.539832
## iter 100 value 14.902598
## final  value 14.902598 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 825.437346 
## iter  10 value 113.175881
## iter  20 value 63.203402
## iter  30 value 45.424468
## iter  40 value 40.453050
## iter  50 value 38.772386
## iter  60 value 37.647873
## iter  70 value 36.772223
## iter  80 value 34.482195
## iter  90 value 33.424518
## iter 100 value 33.002873
## final  value 33.002873 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 906.013037 
## iter  10 value 147.831359
## iter  20 value 101.007794
## iter  30 value 78.210626
## iter  40 value 60.407255
## iter  50 value 56.420746
## iter  60 value 54.260837
## iter  70 value 52.466377
## iter  80 value 51.412580
## iter  90 value 50.724311
## iter 100 value 49.894407
## final  value 49.894407 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1117.280570 
## iter  10 value 114.639969
## iter  20 value 48.178326
## iter  30 value 24.077780
## iter  40 value 19.745141
## iter  50 value 17.299329
## iter  60 value 16.094221
## iter  70 value 15.779231
## iter  80 value 15.334953
## iter  90 value 14.770347
## iter 100 value 14.191959
## final  value 14.191959 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 629.644050 
## iter  10 value 84.276050
## iter  20 value 55.070495
## iter  30 value 35.423223
## iter  40 value 20.571317
## iter  50 value 17.401264
## iter  60 value 14.549082
## iter  70 value 12.346285
## iter  80 value 10.973452
## iter  90 value 10.058351
## iter 100 value 8.801828
## final  value 8.801828 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 794.564690 
## iter  10 value 111.345905
## iter  20 value 68.339023
## iter  30 value 41.798114
## iter  40 value 27.737017
## iter  50 value 15.370208
## iter  60 value 12.484111
## iter  70 value 11.350492
## iter  80 value 9.170244
## iter  90 value 8.523793
## iter 100 value 7.383713
## final  value 7.383713 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 655.674115 
## iter  10 value 74.636306
## iter  20 value 43.769769
## iter  30 value 29.940578
## iter  40 value 13.634790
## iter  50 value 7.939094
## iter  60 value 6.225670
## iter  70 value 5.351534
## iter  80 value 4.924876
## iter  90 value 4.240437
## iter 100 value 4.085335
## final  value 4.085335 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 918.089233 
## iter  10 value 96.783915
## iter  20 value 59.895364
## iter  30 value 48.269837
## iter  40 value 39.361823
## iter  50 value 32.174873
## iter  60 value 28.979478
## iter  70 value 26.100306
## iter  80 value 24.005142
## iter  90 value 22.016428
## iter 100 value 20.727026
## final  value 20.727026 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 930.246048 
## iter  10 value 93.342920
## iter  20 value 59.512720
## iter  30 value 32.163759
## iter  40 value 16.862988
## iter  50 value 14.994385
## iter  60 value 13.851395
## iter  70 value 13.300139
## iter  80 value 12.856177
## iter  90 value 12.345836
## iter 100 value 11.832519
## final  value 11.832519 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1049.087926 
## iter  10 value 122.235123
## iter  20 value 71.818516
## iter  30 value 54.523249
## iter  40 value 40.545645
## iter  50 value 33.252844
## iter  60 value 27.830018
## iter  70 value 25.394312
## iter  80 value 24.023974
## iter  90 value 22.448834
## iter 100 value 21.856033
## final  value 21.856033 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 790.055073 
## iter  10 value 121.360052
## iter  20 value 79.804124
## iter  30 value 45.057953
## iter  40 value 32.511805
## iter  50 value 26.377398
## iter  60 value 22.946149
## iter  70 value 20.239629
## iter  80 value 19.065235
## iter  90 value 17.944163
## iter 100 value 16.126390
## final  value 16.126390 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 970.569908 
## iter  10 value 137.821943
## iter  20 value 84.480142
## iter  30 value 54.071318
## iter  40 value 35.218656
## iter  50 value 25.022342
## iter  60 value 22.093983
## iter  70 value 20.527837
## iter  80 value 19.035254
## iter  90 value 17.991711
## iter 100 value 17.388469
## final  value 17.388469 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 857.122895 
## iter  10 value 112.955765
## iter  20 value 65.981988
## iter  30 value 44.673830
## iter  40 value 33.923742
## iter  50 value 26.107863
## iter  60 value 20.612536
## iter  70 value 17.257654
## iter  80 value 15.593173
## iter  90 value 14.900181
## iter 100 value 11.857550
## final  value 11.857550 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 820.206764 
## iter  10 value 105.387882
## iter  20 value 77.776368
## iter  30 value 56.974628
## iter  40 value 37.047874
## iter  50 value 24.225542
## iter  60 value 20.952749
## iter  70 value 18.692002
## iter  80 value 17.396350
## iter  90 value 16.276939
## iter 100 value 15.615315
## final  value 15.615315 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1433.399586 
## iter  10 value 159.238908
## iter  20 value 99.591267
## iter  30 value 82.978545
## iter  40 value 77.402938
## iter  50 value 72.832409
## iter  60 value 67.849866
## iter  70 value 64.348570
## iter  80 value 58.828497
## iter  90 value 57.854245
## iter 100 value 54.787331
## final  value 54.787331 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 987.509030 
## iter  10 value 88.514533
## iter  20 value 54.200786
## iter  30 value 38.648444
## iter  40 value 32.194655
## iter  50 value 29.190773
## iter  60 value 27.087961
## iter  70 value 23.511305
## iter  80 value 21.924533
## iter  90 value 21.212604
## iter 100 value 20.409518
## final  value 20.409518 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 772.146722 
## iter  10 value 100.857617
## iter  20 value 41.735814
## iter  30 value 19.671969
## iter  40 value 11.778960
## iter  50 value 8.722906
## iter  60 value 7.549774
## iter  70 value 5.901879
## iter  80 value 5.208977
## iter  90 value 4.594935
## iter 100 value 3.914009
## final  value 3.914009 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1150.852770 
## iter  10 value 85.826739
## iter  20 value 50.555533
## iter  30 value 30.750380
## iter  40 value 19.979199
## iter  50 value 16.147845
## iter  60 value 14.921549
## iter  70 value 14.174200
## iter  80 value 13.772738
## iter  90 value 13.594582
## iter 100 value 13.284513
## final  value 13.284513 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1529.367951 
## iter  10 value 227.960974
## iter  20 value 70.338167
## iter  30 value 40.834544
## iter  40 value 34.424791
## iter  50 value 30.721079
## iter  60 value 28.043757
## iter  70 value 25.769551
## iter  80 value 23.581292
## iter  90 value 22.340444
## iter 100 value 21.422372
## final  value 21.422372 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 858.272746 
## iter  10 value 152.093448
## iter  20 value 87.693466
## iter  30 value 54.612556
## iter  40 value 40.319850
## iter  50 value 30.433252
## iter  60 value 26.078599
## iter  70 value 22.558835
## iter  80 value 19.727132
## iter  90 value 18.910606
## iter 100 value 16.658418
## final  value 16.658418 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1164.268753 
## iter  10 value 105.911667
## iter  20 value 69.657753
## iter  30 value 41.826899
## iter  40 value 32.824017
## iter  50 value 31.553805
## iter  60 value 30.731561
## iter  70 value 29.252359
## iter  80 value 28.760363
## iter  90 value 26.485417
## iter 100 value 25.625569
## final  value 25.625569 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1628.575200 
## iter  10 value 108.637440
## iter  20 value 76.866372
## iter  30 value 50.361861
## iter  40 value 37.088628
## iter  50 value 33.291101
## iter  60 value 31.411074
## iter  70 value 29.593744
## iter  80 value 28.075063
## iter  90 value 27.366373
## iter 100 value 26.356028
## final  value 26.356028 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 821.187275 
## iter  10 value 84.706972
## iter  20 value 52.183064
## iter  30 value 35.040389
## iter  40 value 24.136453
## iter  50 value 19.734364
## iter  60 value 18.142733
## iter  70 value 17.553169
## iter  80 value 14.297605
## iter  90 value 12.060334
## iter 100 value 11.798192
## final  value 11.798192 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 900.501444 
## iter  10 value 105.326194
## iter  20 value 66.700860
## iter  30 value 50.145423
## iter  40 value 31.933196
## iter  50 value 24.720206
## iter  60 value 21.845564
## iter  70 value 20.288795
## iter  80 value 19.241288
## iter  90 value 18.729549
## iter 100 value 18.447342
## final  value 18.447342 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1387.184905 
## iter  10 value 93.827422
## iter  20 value 55.142365
## iter  30 value 37.167655
## iter  40 value 23.802584
## iter  50 value 19.367644
## iter  60 value 17.594530
## iter  70 value 16.934238
## iter  80 value 16.544787
## iter  90 value 16.130024
## iter 100 value 15.885580
## final  value 15.885580 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1113.969072 
## iter  10 value 117.194695
## iter  20 value 80.944543
## iter  30 value 64.666233
## iter  40 value 58.292848
## iter  50 value 53.349120
## iter  60 value 52.053568
## iter  70 value 51.397682
## iter  80 value 51.017198
## iter  90 value 50.383821
## iter 100 value 49.656314
## final  value 49.656314 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 832.159033 
## iter  10 value 135.536170
## iter  20 value 85.255523
## iter  30 value 58.268843
## iter  40 value 44.717632
## iter  50 value 41.103285
## iter  60 value 34.127733
## iter  70 value 31.136646
## iter  80 value 30.233400
## iter  90 value 29.725047
## iter 100 value 29.571545
## final  value 29.571545 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 718.782016 
## iter  10 value 112.448468
## iter  20 value 68.673320
## iter  30 value 51.957595
## iter  40 value 34.285967
## iter  50 value 21.334391
## iter  60 value 14.656721
## iter  70 value 10.887174
## iter  80 value 7.803961
## iter  90 value 5.450229
## iter 100 value 5.119959
## final  value 5.119959 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1037.741447 
## iter  10 value 132.381596
## iter  20 value 83.426971
## iter  30 value 49.893703
## iter  40 value 33.471776
## iter  50 value 27.149080
## iter  60 value 24.779460
## iter  70 value 23.501373
## iter  80 value 22.225034
## iter  90 value 21.513093
## iter 100 value 20.870395
## final  value 20.870395 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 707.878954 
## iter  10 value 88.924687
## iter  20 value 44.021352
## iter  30 value 22.546575
## iter  40 value 14.073584
## iter  50 value 10.222164
## iter  60 value 8.605604
## iter  70 value 7.808429
## iter  80 value 7.388818
## iter  90 value 6.558084
## iter 100 value 6.233506
## final  value 6.233506 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 881.130072 
## iter  10 value 163.360001
## iter  20 value 113.601296
## iter  30 value 92.656451
## iter  40 value 75.732075
## iter  50 value 65.244700
## iter  60 value 49.603902
## iter  70 value 38.489106
## iter  80 value 34.749973
## iter  90 value 32.837520
## iter 100 value 31.491817
## final  value 31.491817 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1040.575960 
## iter  10 value 113.693918
## iter  20 value 46.631831
## iter  30 value 26.826095
## iter  40 value 13.410903
## iter  50 value 9.020630
## iter  60 value 8.257545
## iter  70 value 7.701026
## iter  80 value 7.198997
## iter  90 value 6.961354
## iter 100 value 6.787776
## final  value 6.787776 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 966.022366 
## iter  10 value 147.568319
## iter  20 value 77.040944
## iter  30 value 61.318733
## iter  40 value 56.760400
## iter  50 value 46.568092
## iter  60 value 39.954470
## iter  70 value 38.212043
## iter  80 value 37.031358
## iter  90 value 35.302764
## iter 100 value 31.496809
## final  value 31.496809 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 657.483646 
## iter  10 value 166.380545
## iter  20 value 78.675254
## iter  30 value 62.167930
## iter  40 value 49.409712
## iter  50 value 44.130297
## iter  60 value 40.361649
## iter  70 value 38.549312
## iter  80 value 37.550233
## iter  90 value 36.704377
## iter 100 value 35.837077
## final  value 35.837077 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 870.953838 
## iter  10 value 100.673351
## iter  20 value 56.085717
## iter  30 value 34.377880
## iter  40 value 29.135115
## iter  50 value 27.163098
## iter  60 value 24.646777
## iter  70 value 22.563484
## iter  80 value 20.516403
## iter  90 value 18.850271
## iter 100 value 17.971176
## final  value 17.971176 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 898.562320 
## iter  10 value 116.345992
## iter  20 value 75.793279
## iter  30 value 49.697778
## iter  40 value 34.673164
## iter  50 value 31.329766
## iter  60 value 28.245584
## iter  70 value 27.005713
## iter  80 value 24.977459
## iter  90 value 24.414403
## iter 100 value 22.095783
## final  value 22.095783 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1046.781887 
## iter  10 value 89.228966
## iter  20 value 57.812835
## iter  30 value 41.141939
## iter  40 value 33.725527
## iter  50 value 31.167034
## iter  60 value 30.031723
## iter  70 value 27.420618
## iter  80 value 26.006448
## iter  90 value 25.016087
## iter 100 value 24.508638
## final  value 24.508638 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1134.522039 
## iter  10 value 106.310835
## iter  20 value 68.383443
## iter  30 value 47.111405
## iter  40 value 32.722540
## iter  50 value 21.528429
## iter  60 value 19.760643
## iter  70 value 18.773494
## iter  80 value 17.882377
## iter  90 value 17.253760
## iter 100 value 16.426560
## final  value 16.426560 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1128.229515 
## iter  10 value 118.600247
## iter  20 value 84.860937
## iter  30 value 66.811462
## iter  40 value 48.989787
## iter  50 value 41.578661
## iter  60 value 39.685349
## iter  70 value 38.995269
## iter  80 value 38.662343
## iter  90 value 38.150273
## iter 100 value 37.676993
## final  value 37.676993 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 816.390042 
## iter  10 value 109.767896
## iter  20 value 69.666206
## iter  30 value 49.391973
## iter  40 value 39.072634
## iter  50 value 32.818465
## iter  60 value 30.060976
## iter  70 value 27.971573
## iter  80 value 25.540839
## iter  90 value 24.279240
## iter 100 value 23.718784
## final  value 23.718784 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 715.861348 
## iter  10 value 135.593907
## iter  20 value 92.243849
## iter  30 value 63.482469
## iter  40 value 43.944051
## iter  50 value 36.294352
## iter  60 value 33.474719
## iter  70 value 29.110448
## iter  80 value 27.152079
## iter  90 value 26.704470
## iter 100 value 26.441882
## final  value 26.441882 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 864.634041 
## iter  10 value 122.000266
## iter  20 value 92.847189
## iter  30 value 66.146959
## iter  40 value 51.486952
## iter  50 value 45.858980
## iter  60 value 43.056355
## iter  70 value 38.197655
## iter  80 value 36.517957
## iter  90 value 35.418069
## iter 100 value 32.257245
## final  value 32.257245 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 700.339788 
## iter  10 value 147.757010
## iter  20 value 74.395289
## iter  30 value 47.840091
## iter  40 value 38.978477
## iter  50 value 36.526282
## iter  60 value 35.843048
## iter  70 value 35.369023
## iter  80 value 35.031489
## iter  90 value 34.055217
## iter 100 value 33.712478
## final  value 33.712478 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 877.946540 
## iter  10 value 126.803892
## iter  20 value 90.020019
## iter  30 value 68.367737
## iter  40 value 54.377888
## iter  50 value 48.650668
## iter  60 value 43.632334
## iter  70 value 41.138772
## iter  80 value 39.521051
## iter  90 value 38.843521
## iter 100 value 38.270876
## final  value 38.270876 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1155.879962 
## iter  10 value 187.171291
## iter  20 value 129.006002
## iter  30 value 102.851700
## iter  40 value 79.342928
## iter  50 value 63.192246
## iter  60 value 48.475063
## iter  70 value 40.326685
## iter  80 value 37.732761
## iter  90 value 36.195273
## iter 100 value 34.961927
## final  value 34.961927 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1461.342001 
## iter  10 value 84.341981
## iter  20 value 58.491325
## iter  30 value 36.871731
## iter  40 value 25.165599
## iter  50 value 22.729035
## iter  60 value 22.074332
## iter  70 value 21.572861
## iter  80 value 20.306153
## iter  90 value 19.153771
## iter 100 value 14.875741
## final  value 14.875741 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 906.380879 
## iter  10 value 91.270111
## iter  20 value 35.260929
## iter  30 value 15.962967
## iter  40 value 7.889245
## iter  50 value 5.108762
## iter  60 value 3.561081
## iter  70 value 2.943434
## iter  80 value 2.498578
## iter  90 value 2.169579
## iter 100 value 1.954088
## final  value 1.954088 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1070.541738 
## iter  10 value 80.152396
## iter  20 value 44.847990
## iter  30 value 22.376551
## iter  40 value 12.891001
## iter  50 value 7.308593
## iter  60 value 5.308938
## iter  70 value 4.220671
## iter  80 value 3.535550
## iter  90 value 3.290707
## iter 100 value 3.135588
## final  value 3.135588 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 949.982548 
## iter  10 value 84.387550
## iter  20 value 39.590786
## iter  30 value 20.424938
## iter  40 value 12.867072
## iter  50 value 11.460000
## iter  60 value 10.770028
## iter  70 value 10.392522
## iter  80 value 10.094348
## iter  90 value 9.796983
## iter 100 value 9.561152
## final  value 9.561152 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 925.556158 
## iter  10 value 81.922153
## iter  20 value 38.799164
## iter  30 value 23.163803
## iter  40 value 14.545384
## iter  50 value 12.946097
## iter  60 value 11.748857
## iter  70 value 8.305475
## iter  80 value 6.518022
## iter  90 value 5.803527
## iter 100 value 5.242296
## final  value 5.242296 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 820.547411 
## iter  10 value 130.453326
## iter  20 value 89.905224
## iter  30 value 59.146828
## iter  40 value 31.008624
## iter  50 value 16.536702
## iter  60 value 13.081004
## iter  70 value 11.519302
## iter  80 value 10.372295
## iter  90 value 9.394230
## iter 100 value 8.833388
## final  value 8.833388 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 815.242150 
## iter  10 value 119.673694
## iter  20 value 69.944722
## iter  30 value 43.291437
## iter  40 value 32.498994
## iter  50 value 28.656610
## iter  60 value 26.604281
## iter  70 value 23.357679
## iter  80 value 16.728343
## iter  90 value 15.776654
## iter 100 value 14.965709
## final  value 14.965709 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 817.737169 
## iter  10 value 102.168658
## iter  20 value 63.893914
## iter  30 value 48.077588
## iter  40 value 41.380369
## iter  50 value 39.123456
## iter  60 value 33.965638
## iter  70 value 30.883142
## iter  80 value 29.196202
## iter  90 value 28.427488
## iter 100 value 27.103694
## final  value 27.103694 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 824.555013 
## iter  10 value 141.631406
## iter  20 value 83.196923
## iter  30 value 56.635908
## iter  40 value 40.769365
## iter  50 value 30.734144
## iter  60 value 25.723324
## iter  70 value 23.945191
## iter  80 value 22.778379
## iter  90 value 21.073402
## iter 100 value 20.078841
## final  value 20.078841 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1195.641689 
## iter  10 value 78.485106
## iter  20 value 45.450241
## iter  30 value 31.115441
## iter  40 value 20.379933
## iter  50 value 12.649322
## iter  60 value 10.233460
## iter  70 value 9.579823
## iter  80 value 9.276064
## iter  90 value 9.029974
## iter 100 value 8.441181
## final  value 8.441181 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 691.952723 
## iter  10 value 95.946493
## iter  20 value 57.068203
## iter  30 value 38.259698
## iter  40 value 32.056749
## iter  50 value 25.828245
## iter  60 value 22.262509
## iter  70 value 18.968149
## iter  80 value 15.319485
## iter  90 value 12.245900
## iter 100 value 11.083651
## final  value 11.083651 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1604.380800 
## iter  10 value 236.898223
## iter  20 value 118.048771
## iter  30 value 94.763184
## iter  40 value 80.482845
## iter  50 value 74.229049
## iter  60 value 69.880160
## iter  70 value 67.906165
## iter  80 value 66.212871
## iter  90 value 64.609649
## iter 100 value 61.662822
## final  value 61.662822 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 932.356632 
## iter  10 value 161.178568
## iter  20 value 109.951579
## iter  30 value 85.357595
## iter  40 value 55.183059
## iter  50 value 41.002278
## iter  60 value 35.418137
## iter  70 value 31.514229
## iter  80 value 28.457133
## iter  90 value 27.353661
## iter 100 value 25.024541
## final  value 25.024541 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 826.617127 
## iter  10 value 117.000206
## iter  20 value 63.621486
## iter  30 value 41.886112
## iter  40 value 29.085219
## iter  50 value 25.652687
## iter  60 value 24.154009
## iter  70 value 22.923466
## iter  80 value 22.179288
## iter  90 value 21.207882
## iter 100 value 20.253644
## final  value 20.253644 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1062.193656 
## iter  10 value 111.750725
## iter  20 value 72.033417
## iter  30 value 46.050246
## iter  40 value 29.147736
## iter  50 value 22.937211
## iter  60 value 21.514898
## iter  70 value 19.771905
## iter  80 value 17.549459
## iter  90 value 16.366356
## iter 100 value 15.776198
## final  value 15.776198 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 645.388315 
## iter  10 value 180.545239
## iter  20 value 97.220502
## iter  30 value 57.226140
## iter  40 value 38.735730
## iter  50 value 32.089735
## iter  60 value 25.718674
## iter  70 value 22.588270
## iter  80 value 19.963041
## iter  90 value 17.565713
## iter 100 value 13.775011
## final  value 13.775011 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 603.559355 
## iter  10 value 120.507775
## iter  20 value 64.648961
## iter  30 value 41.887640
## iter  40 value 27.159530
## iter  50 value 20.509719
## iter  60 value 18.134474
## iter  70 value 15.976666
## iter  80 value 15.598093
## iter  90 value 15.334394
## iter 100 value 15.012022
## final  value 15.012022 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1166.260548 
## iter  10 value 185.338428
## iter  20 value 130.371435
## iter  30 value 100.364056
## iter  40 value 78.044738
## iter  50 value 58.935708
## iter  60 value 47.077090
## iter  70 value 40.151727
## iter  80 value 35.622018
## iter  90 value 31.515695
## iter 100 value 29.206763
## final  value 29.206763 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 716.428608 
## iter  10 value 112.221457
## iter  20 value 75.296103
## iter  30 value 56.865931
## iter  40 value 49.922416
## iter  50 value 48.112588
## iter  60 value 47.377264
## iter  70 value 40.424392
## iter  80 value 37.772111
## iter  90 value 36.882748
## iter 100 value 36.227910
## final  value 36.227910 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 742.355932 
## iter  10 value 126.822265
## iter  20 value 69.110499
## iter  30 value 42.841958
## iter  40 value 30.686699
## iter  50 value 24.702871
## iter  60 value 21.887224
## iter  70 value 19.471906
## iter  80 value 17.966946
## iter  90 value 16.924108
## iter 100 value 15.153771
## final  value 15.153771 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1614.763688 
## iter  10 value 164.302309
## iter  20 value 125.011640
## iter  30 value 98.388671
## iter  40 value 64.634561
## iter  50 value 47.354042
## iter  60 value 42.285463
## iter  70 value 39.859470
## iter  80 value 38.503516
## iter  90 value 36.258227
## iter 100 value 33.614772
## final  value 33.614772 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1506.265280 
## iter  10 value 118.830364
## iter  20 value 87.983356
## iter  30 value 61.407079
## iter  40 value 47.126665
## iter  50 value 36.899024
## iter  60 value 32.555443
## iter  70 value 30.481253
## iter  80 value 28.141992
## iter  90 value 26.332175
## iter 100 value 24.323719
## final  value 24.323719 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 677.682140 
## iter  10 value 157.528615
## iter  20 value 108.571035
## iter  30 value 91.468412
## iter  40 value 80.260744
## iter  50 value 72.167986
## iter  60 value 65.190795
## iter  70 value 59.544566
## iter  80 value 51.876665
## iter  90 value 46.700043
## iter 100 value 42.025288
## final  value 42.025288 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 922.532703 
## iter  10 value 153.117253
## iter  20 value 92.945861
## iter  30 value 71.317482
## iter  40 value 57.704889
## iter  50 value 49.943288
## iter  60 value 46.675904
## iter  70 value 43.593047
## iter  80 value 42.546506
## iter  90 value 42.022950
## iter 100 value 41.511458
## final  value 41.511458 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 645.978084 
## iter  10 value 99.088157
## iter  20 value 49.350753
## iter  30 value 28.588498
## iter  40 value 23.834823
## iter  50 value 22.042804
## iter  60 value 19.032037
## iter  70 value 18.050364
## iter  80 value 17.470242
## iter  90 value 17.145864
## iter 100 value 16.722103
## final  value 16.722103 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 944.008367 
## iter  10 value 68.385734
## iter  20 value 45.223946
## iter  30 value 21.960801
## iter  40 value 10.953732
## iter  50 value 6.342514
## iter  60 value 4.099181
## iter  70 value 3.131405
## iter  80 value 2.858056
## iter  90 value 2.694577
## iter 100 value 2.555446
## final  value 2.555446 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1218.295285 
## iter  10 value 145.827025
## iter  20 value 102.850889
## iter  30 value 62.401246
## iter  40 value 44.066172
## iter  50 value 30.705109
## iter  60 value 22.938987
## iter  70 value 20.159206
## iter  80 value 15.871789
## iter  90 value 14.682006
## iter 100 value 13.993177
## final  value 13.993177 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 923.424631 
## iter  10 value 127.729974
## iter  20 value 65.962343
## iter  30 value 46.893119
## iter  40 value 32.502869
## iter  50 value 24.104154
## iter  60 value 20.444030
## iter  70 value 17.924559
## iter  80 value 16.633230
## iter  90 value 15.837525
## iter 100 value 15.238200
## final  value 15.238200 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 993.945386 
## iter  10 value 81.165339
## iter  20 value 48.470137
## iter  30 value 33.021472
## iter  40 value 25.926259
## iter  50 value 22.534592
## iter  60 value 20.888732
## iter  70 value 19.385881
## iter  80 value 18.736642
## iter  90 value 18.300653
## iter 100 value 17.819841
## final  value 17.819841 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 831.187512 
## iter  10 value 104.389089
## iter  20 value 60.889862
## iter  30 value 43.420546
## iter  40 value 32.575918
## iter  50 value 26.200542
## iter  60 value 23.336465
## iter  70 value 21.734279
## iter  80 value 19.916509
## iter  90 value 17.827335
## iter 100 value 17.332274
## final  value 17.332274 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 755.709544 
## iter  10 value 94.252761
## iter  20 value 61.708372
## iter  30 value 36.925710
## iter  40 value 30.407844
## iter  50 value 27.852375
## iter  60 value 25.317158
## iter  70 value 24.329904
## iter  80 value 23.028433
## iter  90 value 22.597188
## iter 100 value 22.282799
## final  value 22.282799 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1177.289757 
## iter  10 value 112.884200
## iter  20 value 67.775096
## iter  30 value 50.003517
## iter  40 value 35.242288
## iter  50 value 27.726456
## iter  60 value 25.339824
## iter  70 value 23.979932
## iter  80 value 23.224138
## iter  90 value 22.846024
## iter 100 value 22.329524
## final  value 22.329524 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1047.222105 
## iter  10 value 119.389914
## iter  20 value 73.655107
## iter  30 value 46.345349
## iter  40 value 24.811355
## iter  50 value 13.584146
## iter  60 value 10.711174
## iter  70 value 8.575644
## iter  80 value 6.660931
## iter  90 value 5.562350
## iter 100 value 4.727481
## final  value 4.727481 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 773.079927 
## iter  10 value 97.248853
## iter  20 value 62.848430
## iter  30 value 43.613736
## iter  40 value 33.464397
## iter  50 value 28.202278
## iter  60 value 24.369545
## iter  70 value 20.748755
## iter  80 value 19.784936
## iter  90 value 16.664456
## iter 100 value 14.476549
## final  value 14.476549 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 732.350180 
## iter  10 value 148.820004
## iter  20 value 73.074299
## iter  30 value 56.970587
## iter  40 value 50.706242
## iter  50 value 45.591117
## iter  60 value 42.682270
## iter  70 value 37.375596
## iter  80 value 34.076638
## iter  90 value 31.828936
## iter 100 value 28.910659
## final  value 28.910659 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1078.832455 
## iter  10 value 141.952426
## iter  20 value 101.439650
## iter  30 value 86.655160
## iter  40 value 73.972135
## iter  50 value 66.256948
## iter  60 value 61.628316
## iter  70 value 57.240427
## iter  80 value 55.712253
## iter  90 value 53.852523
## iter 100 value 52.519277
## final  value 52.519277 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 827.464673 
## iter  10 value 129.883637
## iter  20 value 95.448400
## iter  30 value 72.419822
## iter  40 value 50.961204
## iter  50 value 43.425755
## iter  60 value 38.701625
## iter  70 value 35.733188
## iter  80 value 33.700607
## iter  90 value 32.581558
## iter 100 value 31.415884
## final  value 31.415884 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1576.175478 
## iter  10 value 109.427444
## iter  20 value 62.336293
## iter  30 value 42.928109
## iter  40 value 26.465107
## iter  50 value 17.474250
## iter  60 value 13.113873
## iter  70 value 11.661959
## iter  80 value 10.663456
## iter  90 value 9.686482
## iter 100 value 9.226207
## final  value 9.226207 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 613.053629 
## iter  10 value 98.935071
## iter  20 value 47.579567
## iter  30 value 39.024167
## iter  40 value 34.085775
## iter  50 value 31.040375
## iter  60 value 29.163527
## iter  70 value 26.849034
## iter  80 value 25.512784
## iter  90 value 23.659919
## iter 100 value 21.317521
## final  value 21.317521 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1229.757409 
## iter  10 value 181.927523
## iter  20 value 108.672832
## iter  30 value 72.079889
## iter  40 value 49.675051
## iter  50 value 34.527734
## iter  60 value 29.396889
## iter  70 value 27.134588
## iter  80 value 25.353590
## iter  90 value 24.718528
## iter 100 value 24.070007
## final  value 24.070007 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 888.207914 
## iter  10 value 136.044243
## iter  20 value 93.284412
## iter  30 value 74.460801
## iter  40 value 59.911824
## iter  50 value 54.207848
## iter  60 value 46.101918
## iter  70 value 37.218013
## iter  80 value 34.564615
## iter  90 value 33.066055
## iter 100 value 32.121263
## final  value 32.121263 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1436.644886 
## iter  10 value 146.101513
## iter  20 value 85.491253
## iter  30 value 71.915578
## iter  40 value 55.738751
## iter  50 value 46.244657
## iter  60 value 38.989397
## iter  70 value 35.800343
## iter  80 value 34.533407
## iter  90 value 32.319501
## iter 100 value 30.808654
## final  value 30.808654 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 753.682973 
## iter  10 value 93.082129
## iter  20 value 57.124521
## iter  30 value 33.828272
## iter  40 value 27.380808
## iter  50 value 25.491269
## iter  60 value 23.580446
## iter  70 value 20.255790
## iter  80 value 15.161506
## iter  90 value 12.512312
## iter 100 value 11.716445
## final  value 11.716445 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 715.660602 
## iter  10 value 102.155412
## iter  20 value 73.100171
## iter  30 value 34.315602
## iter  40 value 16.627930
## iter  50 value 11.887679
## iter  60 value 10.805730
## iter  70 value 9.915931
## iter  80 value 9.507121
## iter  90 value 8.922459
## iter 100 value 7.037662
## final  value 7.037662 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 649.590749 
## iter  10 value 112.375792
## iter  20 value 60.166298
## iter  30 value 30.974368
## iter  40 value 17.664442
## iter  50 value 11.958450
## iter  60 value 9.130588
## iter  70 value 8.241870
## iter  80 value 7.841109
## iter  90 value 7.544602
## iter 100 value 7.380742
## final  value 7.380742 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 840.598890 
## iter  10 value 234.974477
## iter  20 value 118.219281
## iter  30 value 81.750054
## iter  40 value 56.765450
## iter  50 value 35.990731
## iter  60 value 27.756293
## iter  70 value 23.357136
## iter  80 value 21.213532
## iter  90 value 19.242396
## iter 100 value 17.380563
## final  value 17.380563 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 787.027853 
## iter  10 value 84.380069
## iter  20 value 49.502842
## iter  30 value 27.695008
## iter  40 value 15.965375
## iter  50 value 14.430851
## iter  60 value 13.622529
## iter  70 value 13.143668
## iter  80 value 9.684805
## iter  90 value 8.816235
## iter 100 value 8.454585
## final  value 8.454585 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 861.608801 
## iter  10 value 138.889012
## iter  20 value 72.032827
## iter  30 value 41.968253
## iter  40 value 25.122994
## iter  50 value 20.150552
## iter  60 value 18.947220
## iter  70 value 18.139375
## iter  80 value 17.804070
## iter  90 value 17.504326
## iter 100 value 17.257857
## final  value 17.257857 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 995.300423 
## iter  10 value 186.893163
## iter  20 value 97.854591
## iter  30 value 73.374906
## iter  40 value 64.788632
## iter  50 value 59.665118
## iter  60 value 54.631395
## iter  70 value 52.214099
## iter  80 value 47.765820
## iter  90 value 45.271078
## iter 100 value 33.975397
## final  value 33.975397 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1160.675845 
## iter  10 value 92.875939
## iter  20 value 63.091693
## iter  30 value 30.874431
## iter  40 value 21.762384
## iter  50 value 20.447325
## iter  60 value 19.694345
## iter  70 value 19.203348
## iter  80 value 18.886012
## iter  90 value 18.285857
## iter 100 value 16.371137
## final  value 16.371137 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 737.402102 
## iter  10 value 79.257429
## iter  20 value 43.971932
## iter  30 value 27.949245
## iter  40 value 17.334897
## iter  50 value 13.576341
## iter  60 value 11.961109
## iter  70 value 10.341763
## iter  80 value 8.933748
## iter  90 value 7.705516
## iter 100 value 6.973034
## final  value 6.973034 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 704.602572 
## iter  10 value 102.894446
## iter  20 value 59.168650
## iter  30 value 34.511790
## iter  40 value 18.960682
## iter  50 value 14.712304
## iter  60 value 13.552067
## iter  70 value 12.442884
## iter  80 value 11.965562
## iter  90 value 10.618444
## iter 100 value 9.979084
## final  value 9.979084 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 914.208736 
## iter  10 value 112.033373
## iter  20 value 81.585356
## iter  30 value 55.381634
## iter  40 value 40.152436
## iter  50 value 30.614881
## iter  60 value 25.324942
## iter  70 value 23.063367
## iter  80 value 21.830864
## iter  90 value 21.144979
## iter 100 value 20.489971
## final  value 20.489971 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1333.148196 
## iter  10 value 181.431187
## iter  20 value 107.860688
## iter  30 value 62.825160
## iter  40 value 39.951673
## iter  50 value 24.279314
## iter  60 value 17.902915
## iter  70 value 16.026008
## iter  80 value 14.903508
## iter  90 value 13.468027
## iter 100 value 11.451627
## final  value 11.451627 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 978.656919 
## iter  10 value 145.357072
## iter  20 value 96.621862
## iter  30 value 60.809392
## iter  40 value 45.668271
## iter  50 value 30.181807
## iter  60 value 26.401958
## iter  70 value 23.181556
## iter  80 value 21.021688
## iter  90 value 19.472652
## iter 100 value 18.731741
## final  value 18.731741 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 798.566107 
## iter  10 value 158.431347
## iter  20 value 91.681868
## iter  30 value 67.349133
## iter  40 value 58.457492
## iter  50 value 53.565173
## iter  60 value 50.619327
## iter  70 value 47.961198
## iter  80 value 47.405078
## iter  90 value 46.930580
## iter 100 value 45.763304
## final  value 45.763304 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1188.667863 
## iter  10 value 153.230267
## iter  20 value 106.184731
## iter  30 value 90.749785
## iter  40 value 78.769592
## iter  50 value 68.691305
## iter  60 value 61.983852
## iter  70 value 59.447108
## iter  80 value 57.727944
## iter  90 value 55.132388
## iter 100 value 53.922216
## final  value 53.922216 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1122.104597 
## iter  10 value 120.971564
## iter  20 value 75.800887
## iter  30 value 49.732204
## iter  40 value 40.351898
## iter  50 value 37.097680
## iter  60 value 36.296842
## iter  70 value 35.826253
## iter  80 value 35.173754
## iter  90 value 34.879467
## iter 100 value 34.428316
## final  value 34.428316 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 903.115236 
## iter  10 value 131.778904
## iter  20 value 63.391824
## iter  30 value 46.171920
## iter  40 value 39.463071
## iter  50 value 36.674040
## iter  60 value 35.296644
## iter  70 value 34.981894
## iter  80 value 34.576046
## iter  90 value 34.239284
## iter 100 value 34.038534
## final  value 34.038534 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1286.390369 
## iter  10 value 144.888257
## iter  20 value 89.724827
## iter  30 value 75.722414
## iter  40 value 68.702883
## iter  50 value 62.546117
## iter  60 value 59.362883
## iter  70 value 56.232066
## iter  80 value 55.216430
## iter  90 value 54.069499
## iter 100 value 49.643543
## final  value 49.643543 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 666.035276 
## iter  10 value 106.865097
## iter  20 value 64.033587
## iter  30 value 30.360858
## iter  40 value 14.623682
## iter  50 value 11.968536
## iter  60 value 10.134548
## iter  70 value 7.984359
## iter  80 value 6.514712
## iter  90 value 4.893438
## iter 100 value 4.526645
## final  value 4.526645 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 686.281595 
## iter  10 value 84.742493
## iter  20 value 46.851800
## iter  30 value 20.518266
## iter  40 value 10.874221
## iter  50 value 8.957403
## iter  60 value 8.216497
## iter  70 value 6.981983
## iter  80 value 5.656335
## iter  90 value 4.914416
## iter 100 value 4.676112
## final  value 4.676112 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1084.723798 
## iter  10 value 84.188648
## iter  20 value 54.845948
## iter  30 value 22.085309
## iter  40 value 10.169861
## iter  50 value 4.847787
## iter  60 value 3.397722
## iter  70 value 2.945480
## iter  80 value 2.675276
## iter  90 value 2.496898
## iter 100 value 2.375023
## final  value 2.375023 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 959.171493 
## iter  10 value 83.131529
## iter  20 value 52.984075
## iter  30 value 30.910165
## iter  40 value 21.804971
## iter  50 value 20.086645
## iter  60 value 17.346679
## iter  70 value 16.674136
## iter  80 value 16.086003
## iter  90 value 15.198299
## iter 100 value 11.314348
## final  value 11.314348 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1090.931856 
## iter  10 value 104.016970
## iter  20 value 60.737673
## iter  30 value 25.427618
## iter  40 value 17.195010
## iter  50 value 12.855602
## iter  60 value 11.036595
## iter  70 value 9.898588
## iter  80 value 9.313441
## iter  90 value 8.715665
## iter 100 value 7.930333
## final  value 7.930333 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 976.546279 
## iter  10 value 123.683235
## iter  20 value 77.848664
## iter  30 value 52.902549
## iter  40 value 40.310031
## iter  50 value 32.984056
## iter  60 value 29.760334
## iter  70 value 27.488756
## iter  80 value 24.662077
## iter  90 value 23.741821
## iter 100 value 21.368016
## final  value 21.368016 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 998.798361 
## iter  10 value 115.808912
## iter  20 value 93.361564
## iter  30 value 63.192752
## iter  40 value 49.958051
## iter  50 value 44.166815
## iter  60 value 41.548705
## iter  70 value 38.179062
## iter  80 value 34.323105
## iter  90 value 31.761987
## iter 100 value 28.922786
## final  value 28.922786 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 750.091430 
## iter  10 value 99.096386
## iter  20 value 59.471262
## iter  30 value 31.856104
## iter  40 value 20.580800
## iter  50 value 18.443301
## iter  60 value 17.822805
## iter  70 value 15.741951
## iter  80 value 9.692889
## iter  90 value 8.447048
## iter 100 value 7.506300
## final  value 7.506300 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 975.600894 
## iter  10 value 93.797703
## iter  20 value 49.863608
## iter  30 value 40.470062
## iter  40 value 33.697047
## iter  50 value 28.967731
## iter  60 value 25.429169
## iter  70 value 23.045919
## iter  80 value 21.245629
## iter  90 value 19.104621
## iter 100 value 17.736255
## final  value 17.736255 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 801.042183 
## iter  10 value 132.670033
## iter  20 value 86.287939
## iter  30 value 62.696203
## iter  40 value 46.292177
## iter  50 value 42.354139
## iter  60 value 39.662492
## iter  70 value 37.271452
## iter  80 value 33.872712
## iter  90 value 33.265646
## iter 100 value 32.754973
## final  value 32.754973 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1203.159573 
## iter  10 value 130.788378
## iter  20 value 87.501263
## iter  30 value 71.661790
## iter  40 value 63.307965
## iter  50 value 54.422444
## iter  60 value 46.357776
## iter  70 value 44.200664
## iter  80 value 42.950954
## iter  90 value 42.289999
## iter 100 value 41.736233
## final  value 41.736233 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 970.089165 
## iter  10 value 156.203233
## iter  20 value 96.665339
## iter  30 value 81.102619
## iter  40 value 72.213170
## iter  50 value 62.999667
## iter  60 value 53.560885
## iter  70 value 49.154970
## iter  80 value 43.368304
## iter  90 value 37.116930
## iter 100 value 34.458463
## final  value 34.458463 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 704.466523 
## iter  10 value 101.946212
## iter  20 value 53.102889
## iter  30 value 31.103179
## iter  40 value 19.267053
## iter  50 value 14.722895
## iter  60 value 11.688456
## iter  70 value 7.687743
## iter  80 value 5.196295
## iter  90 value 3.909782
## iter 100 value 3.558805
## final  value 3.558805 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 742.948883 
## iter  10 value 99.179688
## iter  20 value 43.181433
## iter  30 value 20.737812
## iter  40 value 7.861015
## iter  50 value 5.353677
## iter  60 value 4.702161
## iter  70 value 4.082504
## iter  80 value 3.713872
## iter  90 value 3.447340
## iter 100 value 3.270863
## final  value 3.270863 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 748.322230 
## iter  10 value 134.607272
## iter  20 value 93.058927
## iter  30 value 71.143947
## iter  40 value 49.459265
## iter  50 value 31.621733
## iter  60 value 24.038138
## iter  70 value 21.672127
## iter  80 value 20.445294
## iter  90 value 18.901763
## iter 100 value 16.860606
## final  value 16.860606 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 694.556065 
## iter  10 value 127.163651
## iter  20 value 62.619060
## iter  30 value 35.505099
## iter  40 value 18.224546
## iter  50 value 14.459912
## iter  60 value 13.343999
## iter  70 value 12.186256
## iter  80 value 11.362563
## iter  90 value 11.087205
## iter 100 value 10.373127
## final  value 10.373127 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 667.781273 
## iter  10 value 119.848110
## iter  20 value 64.575729
## iter  30 value 39.382564
## iter  40 value 19.519933
## iter  50 value 13.854357
## iter  60 value 12.437196
## iter  70 value 11.346226
## iter  80 value 10.217850
## iter  90 value 9.477542
## iter 100 value 9.046834
## final  value 9.046834 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1930.430450 
## iter  10 value 125.943249
## iter  20 value 78.065567
## iter  30 value 53.276498
## iter  40 value 38.765260
## iter  50 value 29.468935
## iter  60 value 25.478139
## iter  70 value 24.036576
## iter  80 value 23.274401
## iter  90 value 22.587743
## iter 100 value 20.702224
## final  value 20.702224 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 777.978940 
## iter  10 value 95.792478
## iter  20 value 62.981142
## iter  30 value 41.625318
## iter  40 value 33.791814
## iter  50 value 27.615220
## iter  60 value 24.179139
## iter  70 value 22.920156
## iter  80 value 22.214663
## iter  90 value 21.816587
## iter 100 value 21.484591
## final  value 21.484591 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1092.066483 
## iter  10 value 107.706670
## iter  20 value 67.896112
## iter  30 value 44.808558
## iter  40 value 27.941255
## iter  50 value 23.079575
## iter  60 value 21.323320
## iter  70 value 20.136874
## iter  80 value 19.121237
## iter  90 value 17.471209
## iter 100 value 16.414872
## final  value 16.414872 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 879.071871 
## iter  10 value 98.187373
## iter  20 value 58.051081
## iter  30 value 32.287011
## iter  40 value 23.609610
## iter  50 value 21.183624
## iter  60 value 19.637979
## iter  70 value 18.295420
## iter  80 value 15.305260
## iter  90 value 13.826139
## iter 100 value 12.293083
## final  value 12.293083 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 800.640702 
## iter  10 value 100.957652
## iter  20 value 64.233301
## iter  30 value 43.824091
## iter  40 value 29.305842
## iter  50 value 20.084558
## iter  60 value 15.944624
## iter  70 value 14.734822
## iter  80 value 13.328702
## iter  90 value 12.524319
## iter 100 value 11.736975
## final  value 11.736975 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1212.758021 
## iter  10 value 83.706142
## iter  20 value 50.096657
## iter  30 value 28.584693
## iter  40 value 20.180075
## iter  50 value 17.634195
## iter  60 value 16.534883
## iter  70 value 15.773268
## iter  80 value 15.169253
## iter  90 value 14.708150
## iter 100 value 14.351008
## final  value 14.351008 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 799.164846 
## iter  10 value 118.048430
## iter  20 value 62.585574
## iter  30 value 44.265377
## iter  40 value 37.790556
## iter  50 value 32.863331
## iter  60 value 31.110209
## iter  70 value 27.806742
## iter  80 value 25.957823
## iter  90 value 23.031525
## iter 100 value 19.617477
## final  value 19.617477 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 963.661425 
## iter  10 value 93.681439
## iter  20 value 57.185191
## iter  30 value 30.405073
## iter  40 value 16.208599
## iter  50 value 10.482689
## iter  60 value 8.701844
## iter  70 value 7.369790
## iter  80 value 6.146871
## iter  90 value 4.919936
## iter 100 value 4.404099
## final  value 4.404099 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 699.815071 
## iter  10 value 93.434022
## iter  20 value 61.262798
## iter  30 value 36.712407
## iter  40 value 25.467183
## iter  50 value 21.507059
## iter  60 value 17.651906
## iter  70 value 16.281958
## iter  80 value 13.312904
## iter  90 value 9.563659
## iter 100 value 8.823126
## final  value 8.823126 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 983.184909 
## iter  10 value 133.089246
## iter  20 value 77.235414
## iter  30 value 50.462813
## iter  40 value 34.640529
## iter  50 value 28.954258
## iter  60 value 26.928382
## iter  70 value 25.402142
## iter  80 value 24.121357
## iter  90 value 23.005243
## iter 100 value 22.242163
## final  value 22.242163 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 687.488189 
## iter  10 value 115.761809
## iter  20 value 58.438169
## iter  30 value 39.198156
## iter  40 value 30.463869
## iter  50 value 23.509223
## iter  60 value 22.037725
## iter  70 value 20.931389
## iter  80 value 17.871657
## iter  90 value 17.634037
## iter 100 value 17.047310
## final  value 17.047310 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 852.740527 
## iter  10 value 296.826587
## iter  20 value 99.803074
## iter  30 value 65.714968
## iter  40 value 50.267101
## iter  50 value 41.072380
## iter  60 value 32.898226
## iter  70 value 29.975784
## iter  80 value 28.190156
## iter  90 value 26.931078
## iter 100 value 26.377665
## final  value 26.377665 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1287.203902 
## iter  10 value 148.239764
## iter  20 value 71.041989
## iter  30 value 43.533381
## iter  40 value 29.178093
## iter  50 value 19.842087
## iter  60 value 17.220469
## iter  70 value 16.060274
## iter  80 value 15.215011
## iter  90 value 14.763145
## iter 100 value 13.880500
## final  value 13.880500 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 849.795501 
## iter  10 value 127.723723
## iter  20 value 74.944405
## iter  30 value 55.718349
## iter  40 value 47.181400
## iter  50 value 43.505150
## iter  60 value 39.041062
## iter  70 value 34.535666
## iter  80 value 32.275601
## iter  90 value 29.790310
## iter 100 value 28.033459
## final  value 28.033459 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 925.441927 
## iter  10 value 186.257954
## iter  20 value 76.371313
## iter  30 value 46.826057
## iter  40 value 36.065142
## iter  50 value 29.226267
## iter  60 value 26.542728
## iter  70 value 22.735351
## iter  80 value 20.602296
## iter  90 value 19.297230
## iter 100 value 18.220449
## final  value 18.220449 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 683.979654 
## iter  10 value 132.692155
## iter  20 value 78.227511
## iter  30 value 57.057317
## iter  40 value 35.553948
## iter  50 value 22.765953
## iter  60 value 19.490841
## iter  70 value 17.258457
## iter  80 value 15.398829
## iter  90 value 14.009317
## iter 100 value 13.102336
## final  value 13.102336 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 680.865806 
## iter  10 value 97.459084
## iter  20 value 60.839101
## iter  30 value 38.657915
## iter  40 value 26.335660
## iter  50 value 18.174447
## iter  60 value 14.891251
## iter  70 value 12.428250
## iter  80 value 10.328584
## iter  90 value 7.344608
## iter 100 value 6.514914
## final  value 6.514914 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1667.129691 
## iter  10 value 113.667350
## iter  20 value 62.151396
## iter  30 value 45.125681
## iter  40 value 33.484727
## iter  50 value 26.769137
## iter  60 value 22.662861
## iter  70 value 20.985129
## iter  80 value 17.657403
## iter  90 value 17.022655
## iter 100 value 16.421650
## final  value 16.421650 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1139.877512 
## iter  10 value 177.808590
## iter  20 value 106.264487
## iter  30 value 75.811698
## iter  40 value 57.495985
## iter  50 value 49.622241
## iter  60 value 45.006322
## iter  70 value 40.738483
## iter  80 value 39.233750
## iter  90 value 38.757613
## iter 100 value 38.217642
## final  value 38.217642 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 829.067857 
## iter  10 value 103.891907
## iter  20 value 73.674911
## iter  30 value 54.344804
## iter  40 value 38.461989
## iter  50 value 28.775874
## iter  60 value 24.720098
## iter  70 value 19.854152
## iter  80 value 17.820989
## iter  90 value 17.063554
## iter 100 value 16.696932
## final  value 16.696932 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 643.989652 
## iter  10 value 80.143857
## iter  20 value 39.044513
## iter  30 value 21.616970
## iter  40 value 14.747692
## iter  50 value 12.368056
## iter  60 value 11.606387
## iter  70 value 11.098540
## iter  80 value 10.823768
## iter  90 value 10.645636
## iter 100 value 10.464049
## final  value 10.464049 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1332.453300 
## iter  10 value 112.087611
## iter  20 value 67.380567
## iter  30 value 46.612061
## iter  40 value 37.932712
## iter  50 value 33.286219
## iter  60 value 29.897148
## iter  70 value 26.905094
## iter  80 value 26.337871
## iter  90 value 25.679397
## iter 100 value 24.685034
## final  value 24.685034 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 788.372160 
## iter  10 value 104.917023
## iter  20 value 56.307939
## iter  30 value 31.444179
## iter  40 value 24.369388
## iter  50 value 22.632450
## iter  60 value 21.152850
## iter  70 value 20.197147
## iter  80 value 19.134670
## iter  90 value 17.555614
## iter 100 value 16.956952
## final  value 16.956952 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 710.602199 
## iter  10 value 136.316437
## iter  20 value 83.180613
## iter  30 value 51.507611
## iter  40 value 42.284182
## iter  50 value 38.627663
## iter  60 value 36.410689
## iter  70 value 35.695881
## iter  80 value 34.953172
## iter  90 value 33.774200
## iter 100 value 32.302541
## final  value 32.302541 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 887.679857 
## iter  10 value 189.808895
## iter  20 value 104.904571
## iter  30 value 88.102893
## iter  40 value 71.702766
## iter  50 value 66.537245
## iter  60 value 65.092314
## iter  70 value 63.768418
## iter  80 value 61.368705
## iter  90 value 58.164482
## iter 100 value 54.954261
## final  value 54.954261 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 773.789370 
## iter  10 value 118.791392
## iter  20 value 71.051083
## iter  30 value 38.869990
## iter  40 value 25.986375
## iter  50 value 22.630013
## iter  60 value 18.757743
## iter  70 value 17.446224
## iter  80 value 16.710960
## iter  90 value 15.909396
## iter 100 value 14.238387
## final  value 14.238387 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 897.132731 
## iter  10 value 114.811994
## iter  20 value 65.354129
## iter  30 value 46.853051
## iter  40 value 32.468544
## iter  50 value 27.523509
## iter  60 value 19.509632
## iter  70 value 14.758003
## iter  80 value 13.223858
## iter  90 value 12.440398
## iter 100 value 12.032153
## final  value 12.032153 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1196.466479 
## iter  10 value 159.623098
## iter  20 value 85.360089
## iter  30 value 65.259976
## iter  40 value 56.435430
## iter  50 value 48.818013
## iter  60 value 46.222199
## iter  70 value 43.662258
## iter  80 value 42.563220
## iter  90 value 40.195959
## iter 100 value 39.575602
## final  value 39.575602 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 755.583024 
## iter  10 value 103.566071
## iter  20 value 47.890997
## iter  30 value 34.736343
## iter  40 value 29.538059
## iter  50 value 24.343544
## iter  60 value 17.320209
## iter  70 value 10.741910
## iter  80 value 4.990671
## iter  90 value 4.052230
## iter 100 value 3.361908
## final  value 3.361908 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1242.146158 
## iter  10 value 147.077789
## iter  20 value 87.937031
## iter  30 value 62.389784
## iter  40 value 55.850401
## iter  50 value 48.947663
## iter  60 value 46.955147
## iter  70 value 45.635570
## iter  80 value 45.078158
## iter  90 value 44.749429
## iter 100 value 44.108247
## final  value 44.108247 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 816.946900 
## iter  10 value 132.719914
## iter  20 value 93.753648
## iter  30 value 62.549208
## iter  40 value 47.269924
## iter  50 value 35.764616
## iter  60 value 28.350165
## iter  70 value 25.281076
## iter  80 value 24.134161
## iter  90 value 23.505913
## iter 100 value 23.259790
## final  value 23.259790 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 809.111164 
## iter  10 value 103.750482
## iter  20 value 59.669390
## iter  30 value 37.501186
## iter  40 value 24.632648
## iter  50 value 19.575075
## iter  60 value 17.765568
## iter  70 value 16.189959
## iter  80 value 15.301991
## iter  90 value 14.280576
## iter 100 value 13.768064
## final  value 13.768064 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 915.100045 
## iter  10 value 109.420664
## iter  20 value 78.160582
## iter  30 value 53.249188
## iter  40 value 31.834899
## iter  50 value 23.351931
## iter  60 value 20.571855
## iter  70 value 18.637465
## iter  80 value 17.880235
## iter  90 value 17.220655
## iter 100 value 16.292866
## final  value 16.292866 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1263.589125 
## iter  10 value 139.609119
## iter  20 value 78.309996
## iter  30 value 47.013614
## iter  40 value 32.158451
## iter  50 value 28.546568
## iter  60 value 27.058221
## iter  70 value 25.859399
## iter  80 value 24.769940
## iter  90 value 24.264509
## iter 100 value 23.704748
## final  value 23.704748 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 780.160385 
## iter  10 value 123.893357
## iter  20 value 81.088638
## iter  30 value 58.601797
## iter  40 value 46.242922
## iter  50 value 32.030781
## iter  60 value 26.058899
## iter  70 value 24.038492
## iter  80 value 23.014877
## iter  90 value 22.331034
## iter 100 value 21.851062
## final  value 21.851062 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1010.161550 
## iter  10 value 105.501171
## iter  20 value 68.411743
## iter  30 value 52.067718
## iter  40 value 44.043857
## iter  50 value 41.575783
## iter  60 value 39.972392
## iter  70 value 38.239740
## iter  80 value 37.595807
## iter  90 value 37.088670
## iter 100 value 36.678034
## final  value 36.678034 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1423.977386 
## iter  10 value 155.540319
## iter  20 value 98.326193
## iter  30 value 75.401539
## iter  40 value 61.305124
## iter  50 value 53.669507
## iter  60 value 50.514213
## iter  70 value 47.625392
## iter  80 value 45.894591
## iter  90 value 45.080615
## iter 100 value 44.643840
## final  value 44.643840 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1013.064979 
## iter  10 value 116.829706
## iter  20 value 73.548321
## iter  30 value 41.247505
## iter  40 value 30.253929
## iter  50 value 26.641623
## iter  60 value 25.250118
## iter  70 value 24.658761
## iter  80 value 24.382511
## iter  90 value 24.154114
## iter 100 value 24.000522
## final  value 24.000522 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 804.145628 
## iter  10 value 111.635021
## iter  20 value 69.560968
## iter  30 value 56.643021
## iter  40 value 43.452440
## iter  50 value 35.830321
## iter  60 value 30.000858
## iter  70 value 28.613425
## iter  80 value 27.370687
## iter  90 value 26.274144
## iter 100 value 25.037251
## final  value 25.037251 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1037.303229 
## iter  10 value 103.614453
## iter  20 value 76.551572
## iter  30 value 57.895896
## iter  40 value 45.172995
## iter  50 value 39.366356
## iter  60 value 37.689122
## iter  70 value 32.816793
## iter  80 value 29.722755
## iter  90 value 25.995841
## iter 100 value 25.529268
## final  value 25.529268 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1188.831695 
## iter  10 value 101.003689
## iter  20 value 63.532525
## iter  30 value 44.044587
## iter  40 value 36.337645
## iter  50 value 33.081080
## iter  60 value 32.188175
## iter  70 value 30.541606
## iter  80 value 28.984703
## iter  90 value 28.060170
## iter 100 value 27.205607
## final  value 27.205607 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 857.200102 
## iter  10 value 160.921860
## iter  20 value 98.624700
## iter  30 value 74.093740
## iter  40 value 63.293483
## iter  50 value 55.864073
## iter  60 value 52.547161
## iter  70 value 51.056163
## iter  80 value 49.838374
## iter  90 value 48.770221
## iter 100 value 46.524894
## final  value 46.524894 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 919.994278 
## iter  10 value 122.066489
## iter  20 value 85.130452
## iter  30 value 65.683479
## iter  40 value 59.647371
## iter  50 value 56.277405
## iter  60 value 54.498693
## iter  70 value 53.473924
## iter  80 value 52.711660
## iter  90 value 51.752013
## iter 100 value 51.110364
## final  value 51.110364 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1208.971388 
## iter  10 value 126.677786
## iter  20 value 78.421113
## iter  30 value 56.584023
## iter  40 value 42.456945
## iter  50 value 37.820741
## iter  60 value 34.415467
## iter  70 value 31.469388
## iter  80 value 30.345868
## iter  90 value 28.340573
## iter 100 value 26.525890
## final  value 26.525890 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 771.308609 
## iter  10 value 95.963453
## iter  20 value 67.981418
## iter  30 value 43.778152
## iter  40 value 28.599267
## iter  50 value 20.214554
## iter  60 value 17.217222
## iter  70 value 15.705050
## iter  80 value 15.008070
## iter  90 value 14.431127
## iter 100 value 14.145820
## final  value 14.145820 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 782.761442 
## iter  10 value 71.319489
## iter  20 value 45.625115
## iter  30 value 34.495584
## iter  40 value 29.580593
## iter  50 value 26.605425
## iter  60 value 25.583203
## iter  70 value 24.440371
## iter  80 value 23.620390
## iter  90 value 18.831916
## iter 100 value 15.368152
## final  value 15.368152 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1017.568640 
## iter  10 value 110.452242
## iter  20 value 69.718227
## iter  30 value 45.070992
## iter  40 value 34.244479
## iter  50 value 24.871806
## iter  60 value 20.125160
## iter  70 value 16.858653
## iter  80 value 15.270539
## iter  90 value 13.936438
## iter 100 value 13.259083
## final  value 13.259083 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1246.955438 
## iter  10 value 112.240968
## iter  20 value 62.796690
## iter  30 value 44.363436
## iter  40 value 35.416877
## iter  50 value 30.047753
## iter  60 value 28.913682
## iter  70 value 24.587772
## iter  80 value 21.364140
## iter  90 value 20.456935
## iter 100 value 19.530966
## final  value 19.530966 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1192.816508 
## iter  10 value 130.633198
## iter  20 value 79.906372
## iter  30 value 47.241894
## iter  40 value 34.461385
## iter  50 value 29.639301
## iter  60 value 24.662214
## iter  70 value 23.407318
## iter  80 value 21.908416
## iter  90 value 21.367518
## iter 100 value 21.028300
## final  value 21.028300 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 949.067110 
## iter  10 value 126.029272
## iter  20 value 81.054366
## iter  30 value 61.133208
## iter  40 value 45.747999
## iter  50 value 38.851707
## iter  60 value 36.157090
## iter  70 value 31.693919
## iter  80 value 29.187784
## iter  90 value 25.203952
## iter 100 value 23.693202
## final  value 23.693202 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 848.012931 
## iter  10 value 143.597522
## iter  20 value 86.993909
## iter  30 value 69.181351
## iter  40 value 53.821300
## iter  50 value 47.817816
## iter  60 value 45.581099
## iter  70 value 44.352347
## iter  80 value 43.618756
## iter  90 value 41.376089
## iter 100 value 40.660504
## final  value 40.660504 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 885.412130 
## iter  10 value 146.403330
## iter  20 value 79.122463
## iter  30 value 55.973741
## iter  40 value 45.543977
## iter  50 value 41.216460
## iter  60 value 38.343041
## iter  70 value 36.568362
## iter  80 value 35.401842
## iter  90 value 30.939434
## iter 100 value 29.666269
## final  value 29.666269 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1093.297446 
## iter  10 value 117.568617
## iter  20 value 81.075880
## iter  30 value 55.404354
## iter  40 value 37.754665
## iter  50 value 31.106569
## iter  60 value 28.840061
## iter  70 value 25.133260
## iter  80 value 24.366423
## iter  90 value 23.509439
## iter 100 value 22.256744
## final  value 22.256744 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1318.798358 
## iter  10 value 166.405890
## iter  20 value 102.476628
## iter  30 value 76.560086
## iter  40 value 64.601760
## iter  50 value 58.580116
## iter  60 value 53.850770
## iter  70 value 50.536753
## iter  80 value 49.563107
## iter  90 value 49.030724
## iter 100 value 48.136654
## final  value 48.136654 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1500.747041 
## iter  10 value 200.088371
## iter  20 value 131.319984
## iter  30 value 109.971735
## iter  40 value 96.016060
## iter  50 value 85.202950
## iter  60 value 80.315830
## iter  70 value 77.026117
## iter  80 value 73.743879
## iter  90 value 72.205377
## iter 100 value 70.886998
## final  value 70.886998 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 717.104478 
## iter  10 value 103.201279
## iter  20 value 72.902223
## iter  30 value 53.418885
## iter  40 value 39.860123
## iter  50 value 36.419153
## iter  60 value 33.273905
## iter  70 value 30.837049
## iter  80 value 29.441095
## iter  90 value 28.527519
## iter 100 value 26.564929
## final  value 26.564929 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1244.886725 
## iter  10 value 170.333055
## iter  20 value 94.948213
## iter  30 value 63.773410
## iter  40 value 47.090742
## iter  50 value 38.820641
## iter  60 value 29.511717
## iter  70 value 24.622809
## iter  80 value 21.810226
## iter  90 value 20.080405
## iter 100 value 19.077397
## final  value 19.077397 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1286.597773 
## iter  10 value 113.506279
## iter  20 value 78.286334
## iter  30 value 49.970159
## iter  40 value 35.037983
## iter  50 value 31.266557
## iter  60 value 29.192103
## iter  70 value 25.279687
## iter  80 value 24.216055
## iter  90 value 23.447885
## iter 100 value 22.945966
## final  value 22.945966 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 725.383705 
## iter  10 value 112.106303
## iter  20 value 69.116422
## iter  30 value 45.001518
## iter  40 value 30.357346
## iter  50 value 24.204015
## iter  60 value 21.987986
## iter  70 value 21.018151
## iter  80 value 20.615562
## iter  90 value 20.170138
## iter 100 value 19.507214
## final  value 19.507214 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 765.084571 
## iter  10 value 188.326186
## iter  20 value 142.107220
## iter  30 value 99.004005
## iter  40 value 84.844191
## iter  50 value 65.772789
## iter  60 value 57.905967
## iter  70 value 52.091244
## iter  80 value 32.439282
## iter  90 value 29.067476
## iter 100 value 28.247721
## final  value 28.247721 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 982.497231 
## iter  10 value 150.112304
## iter  20 value 83.165710
## iter  30 value 56.954663
## iter  40 value 49.199927
## iter  50 value 45.916518
## iter  60 value 39.476337
## iter  70 value 37.376932
## iter  80 value 34.760932
## iter  90 value 32.589264
## iter 100 value 31.325665
## final  value 31.325665 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1479.867580 
## iter  10 value 105.145168
## iter  20 value 54.040771
## iter  30 value 34.931589
## iter  40 value 26.118300
## iter  50 value 23.681100
## iter  60 value 22.021751
## iter  70 value 20.255342
## iter  80 value 19.758659
## iter  90 value 19.342500
## iter 100 value 19.039874
## final  value 19.039874 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 668.508121 
## iter  10 value 109.734710
## iter  20 value 61.632419
## iter  30 value 35.064115
## iter  40 value 17.559829
## iter  50 value 13.833536
## iter  60 value 12.910993
## iter  70 value 12.162925
## iter  80 value 11.532633
## iter  90 value 10.957339
## iter 100 value 9.301939
## final  value 9.301939 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 744.265384 
## iter  10 value 96.534038
## iter  20 value 57.426260
## iter  30 value 25.739059
## iter  40 value 16.183234
## iter  50 value 10.561271
## iter  60 value 7.613261
## iter  70 value 7.120983
## iter  80 value 6.810812
## iter  90 value 6.552834
## iter 100 value 6.250758
## final  value 6.250758 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 703.016738 
## iter  10 value 112.362406
## iter  20 value 63.824293
## iter  30 value 37.348070
## iter  40 value 27.047140
## iter  50 value 22.595060
## iter  60 value 20.977434
## iter  70 value 19.448040
## iter  80 value 18.660430
## iter  90 value 18.097906
## iter 100 value 17.313405
## final  value 17.313405 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1114.692918 
## iter  10 value 159.044634
## iter  20 value 112.208157
## iter  30 value 83.748296
## iter  40 value 60.994315
## iter  50 value 51.992397
## iter  60 value 42.770745
## iter  70 value 37.724431
## iter  80 value 35.234266
## iter  90 value 33.743650
## iter 100 value 32.641034
## final  value 32.641034 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1183.269900 
## iter  10 value 129.233116
## iter  20 value 88.605290
## iter  30 value 71.626275
## iter  40 value 52.754534
## iter  50 value 40.337070
## iter  60 value 34.030073
## iter  70 value 30.980725
## iter  80 value 28.985506
## iter  90 value 27.985600
## iter 100 value 27.421625
## final  value 27.421625 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 737.701005 
## iter  10 value 105.804038
## iter  20 value 65.148890
## iter  30 value 34.076281
## iter  40 value 20.827170
## iter  50 value 15.700069
## iter  60 value 13.794605
## iter  70 value 12.656141
## iter  80 value 12.064852
## iter  90 value 11.662121
## iter 100 value 11.368178
## final  value 11.368178 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 976.474187 
## iter  10 value 113.682118
## iter  20 value 67.886362
## iter  30 value 42.198721
## iter  40 value 18.963673
## iter  50 value 14.589338
## iter  60 value 13.438620
## iter  70 value 12.387907
## iter  80 value 10.326221
## iter  90 value 9.600571
## iter 100 value 8.982548
## final  value 8.982548 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 983.465786 
## iter  10 value 128.544768
## iter  20 value 90.578919
## iter  30 value 72.634727
## iter  40 value 64.256021
## iter  50 value 61.179122
## iter  60 value 59.487543
## iter  70 value 56.612484
## iter  80 value 53.590193
## iter  90 value 48.465303
## iter 100 value 42.663990
## final  value 42.663990 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 849.301257 
## iter  10 value 165.215179
## iter  20 value 90.478704
## iter  30 value 73.117870
## iter  40 value 52.697440
## iter  50 value 42.720302
## iter  60 value 38.312908
## iter  70 value 36.524711
## iter  80 value 34.252288
## iter  90 value 32.710919
## iter 100 value 31.517049
## final  value 31.517049 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1512.356852 
## iter  10 value 136.065710
## iter  20 value 74.834238
## iter  30 value 50.690011
## iter  40 value 40.014837
## iter  50 value 31.013657
## iter  60 value 26.446622
## iter  70 value 21.900115
## iter  80 value 17.980143
## iter  90 value 17.119874
## iter 100 value 16.544588
## final  value 16.544588 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1048.196149 
## iter  10 value 192.195136
## iter  20 value 120.503895
## iter  30 value 93.408779
## iter  40 value 68.799954
## iter  50 value 55.033041
## iter  60 value 42.547455
## iter  70 value 39.748576
## iter  80 value 38.521534
## iter  90 value 37.470719
## iter 100 value 36.060325
## final  value 36.060325 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1101.071740 
## iter  10 value 103.445569
## iter  20 value 78.399751
## iter  30 value 56.342682
## iter  40 value 47.677678
## iter  50 value 43.595831
## iter  60 value 41.364156
## iter  70 value 39.682284
## iter  80 value 37.419985
## iter  90 value 35.573899
## iter 100 value 34.835620
## final  value 34.835620 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1064.556334 
## iter  10 value 115.938816
## iter  20 value 69.704934
## iter  30 value 46.678354
## iter  40 value 28.803827
## iter  50 value 23.849162
## iter  60 value 22.181944
## iter  70 value 20.956745
## iter  80 value 20.172900
## iter  90 value 19.638641
## iter 100 value 19.289013
## final  value 19.289013 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 925.306912 
## iter  10 value 101.736040
## iter  20 value 64.452195
## iter  30 value 40.263533
## iter  40 value 36.218000
## iter  50 value 34.710895
## iter  60 value 32.856643
## iter  70 value 28.147716
## iter  80 value 23.425921
## iter  90 value 21.696521
## iter 100 value 17.872144
## final  value 17.872144 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1162.876607 
## iter  10 value 123.422523
## iter  20 value 68.531967
## iter  30 value 45.576868
## iter  40 value 34.800270
## iter  50 value 29.560856
## iter  60 value 28.136098
## iter  70 value 26.398917
## iter  80 value 24.980866
## iter  90 value 23.596928
## iter 100 value 22.529622
## final  value 22.529622 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 765.803802 
## iter  10 value 111.993577
## iter  20 value 81.435175
## iter  30 value 58.857599
## iter  40 value 39.616649
## iter  50 value 35.199194
## iter  60 value 32.639846
## iter  70 value 30.569958
## iter  80 value 27.870095
## iter  90 value 25.855419
## iter 100 value 25.304513
## final  value 25.304513 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1158.634808 
## iter  10 value 151.018145
## iter  20 value 108.046731
## iter  30 value 84.408933
## iter  40 value 56.153036
## iter  50 value 44.442457
## iter  60 value 40.188897
## iter  70 value 38.535093
## iter  80 value 37.724010
## iter  90 value 36.793139
## iter 100 value 36.342216
## final  value 36.342216 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 808.774416 
## iter  10 value 198.836063
## iter  20 value 151.330966
## iter  30 value 130.286013
## iter  40 value 107.482308
## iter  50 value 97.065207
## iter  60 value 89.497204
## iter  70 value 83.352611
## iter  80 value 81.008444
## iter  90 value 79.071710
## iter 100 value 75.438289
## final  value 75.438289 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 857.184367 
## iter  10 value 144.232080
## iter  20 value 82.582562
## iter  30 value 60.769445
## iter  40 value 49.616094
## iter  50 value 40.825259
## iter  60 value 29.123932
## iter  70 value 23.448829
## iter  80 value 22.465470
## iter  90 value 19.632411
## iter 100 value 17.831211
## final  value 17.831211 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1018.953052 
## iter  10 value 149.711689
## iter  20 value 73.549547
## iter  30 value 42.697514
## iter  40 value 27.569470
## iter  50 value 19.334268
## iter  60 value 17.494577
## iter  70 value 16.167767
## iter  80 value 15.379304
## iter  90 value 14.587702
## iter 100 value 14.073665
## final  value 14.073665 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 872.024822 
## iter  10 value 113.559586
## iter  20 value 77.906263
## iter  30 value 47.000430
## iter  40 value 36.906265
## iter  50 value 30.624730
## iter  60 value 21.877159
## iter  70 value 17.926326
## iter  80 value 16.446260
## iter  90 value 15.578069
## iter 100 value 15.203897
## final  value 15.203897 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 835.095584 
## iter  10 value 89.795571
## iter  20 value 48.551516
## iter  30 value 23.841256
## iter  40 value 15.916861
## iter  50 value 13.784020
## iter  60 value 13.160443
## iter  70 value 12.679062
## iter  80 value 11.812056
## iter  90 value 11.205037
## iter 100 value 10.931020
## final  value 10.931020 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 2614.876773 
## iter  10 value 138.964774
## iter  20 value 78.227196
## iter  30 value 62.390707
## iter  40 value 57.025969
## iter  50 value 52.084813
## iter  60 value 43.035542
## iter  70 value 34.260752
## iter  80 value 30.778321
## iter  90 value 27.120343
## iter 100 value 25.576463
## final  value 25.576463 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1855.226695 
## iter  10 value 100.391261
## iter  20 value 67.171732
## iter  30 value 46.241575
## iter  40 value 25.992958
## iter  50 value 14.025467
## iter  60 value 11.247210
## iter  70 value 9.855698
## iter  80 value 6.890624
## iter  90 value 4.779428
## iter 100 value 4.152623
## final  value 4.152623 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1313.302520 
## iter  10 value 107.742531
## iter  20 value 47.214854
## iter  30 value 22.393633
## iter  40 value 15.363039
## iter  50 value 11.818790
## iter  60 value 10.222873
## iter  70 value 8.101742
## iter  80 value 7.173527
## iter  90 value 6.097089
## iter 100 value 4.040679
## final  value 4.040679 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 643.596342 
## iter  10 value 59.892902
## iter  20 value 29.638568
## iter  30 value 18.767665
## iter  40 value 16.574426
## iter  50 value 12.670981
## iter  60 value 10.782152
## iter  70 value 9.687050
## iter  80 value 7.964381
## iter  90 value 6.771767
## iter 100 value 5.582400
## final  value 5.582400 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1001.718527 
## iter  10 value 80.291342
## iter  20 value 30.323977
## iter  30 value 15.684674
## iter  40 value 8.582161
## iter  50 value 4.197978
## iter  60 value 2.869367
## iter  70 value 2.503065
## iter  80 value 2.323130
## iter  90 value 2.177287
## iter 100 value 2.116280
## final  value 2.116280 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 669.774370 
## iter  10 value 92.796639
## iter  20 value 51.036084
## iter  30 value 25.716715
## iter  40 value 10.573169
## iter  50 value 8.915984
## iter  60 value 8.012317
## iter  70 value 7.292117
## iter  80 value 5.834378
## iter  90 value 4.780609
## iter 100 value 4.354950
## final  value 4.354950 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1316.056144 
## iter  10 value 103.578697
## iter  20 value 57.143659
## iter  30 value 36.493346
## iter  40 value 19.503089
## iter  50 value 9.791526
## iter  60 value 5.539887
## iter  70 value 4.627889
## iter  80 value 4.073573
## iter  90 value 3.749367
## iter 100 value 3.439983
## final  value 3.439983 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 618.637360 
## iter  10 value 111.428958
## iter  20 value 72.520777
## iter  30 value 55.135757
## iter  40 value 37.001927
## iter  50 value 28.687970
## iter  60 value 25.560606
## iter  70 value 22.428044
## iter  80 value 20.551782
## iter  90 value 20.128692
## iter 100 value 19.450989
## final  value 19.450989 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1128.242302 
## iter  10 value 129.348608
## iter  20 value 72.123020
## iter  30 value 43.467332
## iter  40 value 33.139753
## iter  50 value 30.556544
## iter  60 value 28.491253
## iter  70 value 27.095830
## iter  80 value 26.091274
## iter  90 value 24.407783
## iter 100 value 23.078902
## final  value 23.078902 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 681.211823 
## iter  10 value 112.266915
## iter  20 value 68.579285
## iter  30 value 40.493788
## iter  40 value 29.473391
## iter  50 value 26.092129
## iter  60 value 23.976540
## iter  70 value 23.305581
## iter  80 value 22.758147
## iter  90 value 21.114436
## iter 100 value 19.084113
## final  value 19.084113 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 925.434907 
## iter  10 value 120.798259
## iter  20 value 76.306749
## iter  30 value 56.640446
## iter  40 value 38.648802
## iter  50 value 24.390034
## iter  60 value 18.871170
## iter  70 value 17.125397
## iter  80 value 15.512121
## iter  90 value 14.706719
## iter 100 value 14.267173
## final  value 14.267173 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 843.330138 
## iter  10 value 101.125820
## iter  20 value 57.687282
## iter  30 value 29.103583
## iter  40 value 15.897452
## iter  50 value 10.806845
## iter  60 value 9.427413
## iter  70 value 8.342497
## iter  80 value 7.700418
## iter  90 value 7.415874
## iter 100 value 6.975170
## final  value 6.975170 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1310.497267 
## iter  10 value 149.567787
## iter  20 value 106.781737
## iter  30 value 74.173792
## iter  40 value 48.585000
## iter  50 value 35.245807
## iter  60 value 29.909005
## iter  70 value 28.492837
## iter  80 value 27.411981
## iter  90 value 25.588086
## iter 100 value 24.183064
## final  value 24.183064 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1801.742438 
## iter  10 value 107.196249
## iter  20 value 54.698633
## iter  30 value 37.826937
## iter  40 value 30.613120
## iter  50 value 26.548262
## iter  60 value 25.298246
## iter  70 value 22.671279
## iter  80 value 21.470550
## iter  90 value 20.743566
## iter 100 value 19.422518
## final  value 19.422518 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 794.965726 
## iter  10 value 97.159146
## iter  20 value 67.584150
## iter  30 value 50.544310
## iter  40 value 40.084395
## iter  50 value 34.040450
## iter  60 value 31.579092
## iter  70 value 28.578526
## iter  80 value 27.217417
## iter  90 value 26.204830
## iter 100 value 25.317298
## final  value 25.317298 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 989.808921 
## iter  10 value 113.382809
## iter  20 value 81.856531
## iter  30 value 60.752972
## iter  40 value 39.587186
## iter  50 value 31.551049
## iter  60 value 26.598254
## iter  70 value 23.966387
## iter  80 value 20.526108
## iter  90 value 18.005547
## iter 100 value 16.634897
## final  value 16.634897 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1244.726624 
## iter  10 value 90.674495
## iter  20 value 57.160397
## iter  30 value 31.337304
## iter  40 value 12.763744
## iter  50 value 9.615036
## iter  60 value 8.611587
## iter  70 value 7.922884
## iter  80 value 7.290200
## iter  90 value 6.591097
## iter 100 value 6.129707
## final  value 6.129707 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1125.363113 
## iter  10 value 117.424650
## iter  20 value 60.072800
## iter  30 value 45.752645
## iter  40 value 39.214014
## iter  50 value 34.784311
## iter  60 value 32.131514
## iter  70 value 30.979897
## iter  80 value 30.469863
## iter  90 value 30.274551
## iter 100 value 30.135174
## final  value 30.135174 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1308.227587 
## iter  10 value 193.075276
## iter  20 value 92.410569
## iter  30 value 72.411616
## iter  40 value 61.941417
## iter  50 value 56.260171
## iter  60 value 49.467481
## iter  70 value 43.080182
## iter  80 value 38.683816
## iter  90 value 34.999633
## iter 100 value 32.344093
## final  value 32.344093 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 891.159956 
## iter  10 value 110.274519
## iter  20 value 60.429573
## iter  30 value 37.488406
## iter  40 value 27.503841
## iter  50 value 24.056729
## iter  60 value 22.755008
## iter  70 value 21.867420
## iter  80 value 21.361398
## iter  90 value 20.961775
## iter 100 value 20.207347
## final  value 20.207347 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 902.161358 
## iter  10 value 99.372244
## iter  20 value 67.119334
## iter  30 value 43.926271
## iter  40 value 21.211144
## iter  50 value 11.018170
## iter  60 value 6.635193
## iter  70 value 4.773271
## iter  80 value 4.293777
## iter  90 value 3.976633
## iter 100 value 3.831670
## final  value 3.831670 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1139.478240 
## iter  10 value 115.457873
## iter  20 value 65.293327
## iter  30 value 52.839759
## iter  40 value 45.835393
## iter  50 value 44.044262
## iter  60 value 43.173404
## iter  70 value 42.863009
## iter  80 value 42.595372
## iter  90 value 42.247898
## iter 100 value 41.659744
## final  value 41.659744 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1062.102952 
## iter  10 value 115.416535
## iter  20 value 71.606266
## iter  30 value 54.618273
## iter  40 value 42.703696
## iter  50 value 38.137243
## iter  60 value 35.525959
## iter  70 value 30.123082
## iter  80 value 28.718066
## iter  90 value 27.793050
## iter 100 value 25.492885
## final  value 25.492885 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 651.273220 
## iter  10 value 106.288936
## iter  20 value 64.216211
## iter  30 value 38.486919
## iter  40 value 29.232129
## iter  50 value 24.913136
## iter  60 value 23.467228
## iter  70 value 19.949422
## iter  80 value 17.586955
## iter  90 value 14.163508
## iter 100 value 13.758380
## final  value 13.758380 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1411.066133 
## iter  10 value 200.362939
## iter  20 value 114.073358
## iter  30 value 85.076801
## iter  40 value 77.737922
## iter  50 value 73.157864
## iter  60 value 69.973968
## iter  70 value 66.031225
## iter  80 value 63.013628
## iter  90 value 61.498719
## iter 100 value 59.063846
## final  value 59.063846 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 941.452427 
## iter  10 value 94.305486
## iter  20 value 57.195288
## iter  30 value 32.682803
## iter  40 value 21.518048
## iter  50 value 16.691414
## iter  60 value 13.901296
## iter  70 value 11.922324
## iter  80 value 10.985429
## iter  90 value 9.829397
## iter 100 value 9.308620
## final  value 9.308620 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 928.920421 
## iter  10 value 91.552236
## iter  20 value 53.002575
## iter  30 value 42.125898
## iter  40 value 28.474366
## iter  50 value 22.796701
## iter  60 value 20.679953
## iter  70 value 19.322888
## iter  80 value 17.192965
## iter  90 value 16.320444
## iter 100 value 15.862065
## final  value 15.862065 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 694.380381 
## iter  10 value 107.934136
## iter  20 value 64.928846
## iter  30 value 38.526495
## iter  40 value 27.420224
## iter  50 value 22.289606
## iter  60 value 21.174856
## iter  70 value 20.565645
## iter  80 value 20.240967
## iter  90 value 20.010163
## iter 100 value 19.528615
## final  value 19.528615 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 799.215600 
## iter  10 value 97.644550
## iter  20 value 70.386962
## iter  30 value 43.401183
## iter  40 value 32.642779
## iter  50 value 21.256059
## iter  60 value 19.097378
## iter  70 value 18.344391
## iter  80 value 15.799393
## iter  90 value 15.228753
## iter 100 value 14.959321
## final  value 14.959321 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 705.963160 
## iter  10 value 82.062447
## iter  20 value 33.213463
## iter  30 value 17.082182
## iter  40 value 6.396644
## iter  50 value 3.472983
## iter  60 value 3.004936
## iter  70 value 2.680889
## iter  80 value 2.472310
## iter  90 value 2.248716
## iter 100 value 2.083130
## final  value 2.083130 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1202.251429 
## iter  10 value 141.771463
## iter  20 value 75.894557
## iter  30 value 39.799039
## iter  40 value 24.371888
## iter  50 value 20.564786
## iter  60 value 19.465189
## iter  70 value 18.677623
## iter  80 value 17.263778
## iter  90 value 16.343946
## iter 100 value 16.127547
## final  value 16.127547 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 822.258873 
## iter  10 value 137.616503
## iter  20 value 87.111844
## iter  30 value 71.933603
## iter  40 value 66.606920
## iter  50 value 62.380021
## iter  60 value 57.925791
## iter  70 value 54.341291
## iter  80 value 50.853712
## iter  90 value 49.218597
## iter 100 value 47.759980
## final  value 47.759980 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1139.099627 
## iter  10 value 102.346436
## iter  20 value 61.616992
## iter  30 value 41.404340
## iter  40 value 29.082847
## iter  50 value 23.868184
## iter  60 value 21.676986
## iter  70 value 20.507540
## iter  80 value 19.186456
## iter  90 value 18.046796
## iter 100 value 16.594898
## final  value 16.594898 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1440.268784 
## iter  10 value 94.566568
## iter  20 value 55.902022
## iter  30 value 39.603767
## iter  40 value 28.168363
## iter  50 value 21.849157
## iter  60 value 19.455834
## iter  70 value 18.045985
## iter  80 value 17.066342
## iter  90 value 16.446887
## iter 100 value 14.867254
## final  value 14.867254 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 868.589682 
## iter  10 value 87.780184
## iter  20 value 40.629760
## iter  30 value 23.462170
## iter  40 value 15.735315
## iter  50 value 11.493857
## iter  60 value 9.713263
## iter  70 value 9.242671
## iter  80 value 8.894662
## iter  90 value 8.088157
## iter 100 value 7.848878
## final  value 7.848878 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1031.924821 
## iter  10 value 122.178696
## iter  20 value 72.819238
## iter  30 value 47.561189
## iter  40 value 36.582186
## iter  50 value 33.354319
## iter  60 value 31.637162
## iter  70 value 31.167352
## iter  80 value 30.548677
## iter  90 value 30.004200
## iter 100 value 29.593224
## final  value 29.593224 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1173.862952 
## iter  10 value 126.724493
## iter  20 value 67.501069
## iter  30 value 40.889229
## iter  40 value 25.597680
## iter  50 value 19.557472
## iter  60 value 17.688795
## iter  70 value 17.203390
## iter  80 value 16.665420
## iter  90 value 16.178475
## iter 100 value 15.527547
## final  value 15.527547 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 707.115956 
## iter  10 value 116.501945
## iter  20 value 80.410384
## iter  30 value 55.005623
## iter  40 value 43.966127
## iter  50 value 39.298840
## iter  60 value 37.032820
## iter  70 value 34.905550
## iter  80 value 33.766464
## iter  90 value 32.740680
## iter 100 value 31.644463
## final  value 31.644463 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 804.384052 
## iter  10 value 117.502929
## iter  20 value 77.619549
## iter  30 value 50.567518
## iter  40 value 39.161274
## iter  50 value 34.674056
## iter  60 value 30.937981
## iter  70 value 29.144615
## iter  80 value 28.312308
## iter  90 value 27.256064
## iter 100 value 26.583606
## final  value 26.583606 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 758.271739 
## iter  10 value 161.609010
## iter  20 value 96.034719
## iter  30 value 75.916287
## iter  40 value 60.402852
## iter  50 value 46.854564
## iter  60 value 38.933783
## iter  70 value 34.735679
## iter  80 value 32.251051
## iter  90 value 28.694373
## iter 100 value 26.093671
## final  value 26.093671 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 829.962015 
## iter  10 value 108.233320
## iter  20 value 58.811588
## iter  30 value 38.144498
## iter  40 value 33.145312
## iter  50 value 31.388261
## iter  60 value 30.587026
## iter  70 value 30.299924
## iter  80 value 30.070575
## iter  90 value 29.934667
## iter 100 value 29.631911
## final  value 29.631911 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1127.044586 
## iter  10 value 104.854159
## iter  20 value 67.204683
## iter  30 value 38.707627
## iter  40 value 20.538750
## iter  50 value 16.685461
## iter  60 value 15.549278
## iter  70 value 14.444839
## iter  80 value 13.795980
## iter  90 value 13.389885
## iter 100 value 13.156907
## final  value 13.156907 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 872.176538 
## iter  10 value 157.414755
## iter  20 value 100.058603
## iter  30 value 76.025051
## iter  40 value 42.364054
## iter  50 value 29.742492
## iter  60 value 25.728987
## iter  70 value 24.381717
## iter  80 value 23.617921
## iter  90 value 23.000039
## iter 100 value 22.612791
## final  value 22.612791 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 977.729314 
## iter  10 value 132.593862
## iter  20 value 92.838236
## iter  30 value 71.410770
## iter  40 value 58.249016
## iter  50 value 43.239355
## iter  60 value 37.831266
## iter  70 value 36.240067
## iter  80 value 34.296015
## iter  90 value 32.859008
## iter 100 value 31.923694
## final  value 31.923694 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1332.000452 
## iter  10 value 107.468222
## iter  20 value 78.354344
## iter  30 value 49.481774
## iter  40 value 32.660105
## iter  50 value 23.622079
## iter  60 value 21.479627
## iter  70 value 20.072227
## iter  80 value 19.076455
## iter  90 value 18.615434
## iter 100 value 17.502519
## final  value 17.502519 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1777.552779 
## iter  10 value 127.948556
## iter  20 value 75.979727
## iter  30 value 56.861721
## iter  40 value 51.494227
## iter  50 value 48.658596
## iter  60 value 46.128190
## iter  70 value 44.778716
## iter  80 value 43.952390
## iter  90 value 43.444735
## iter 100 value 42.707583
## final  value 42.707583 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 721.405272 
## iter  10 value 159.617116
## iter  20 value 119.440638
## iter  30 value 102.316501
## iter  40 value 88.669028
## iter  50 value 77.492241
## iter  60 value 67.142025
## iter  70 value 59.816117
## iter  80 value 55.006506
## iter  90 value 51.986744
## iter 100 value 48.809707
## final  value 48.809707 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 881.003257 
## iter  10 value 149.943144
## iter  20 value 99.192326
## iter  30 value 62.317486
## iter  40 value 44.137504
## iter  50 value 36.531300
## iter  60 value 33.595170
## iter  70 value 32.617208
## iter  80 value 31.971963
## iter  90 value 31.641724
## iter 100 value 31.429740
## final  value 31.429740 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1490.216566 
## iter  10 value 131.936911
## iter  20 value 88.077524
## iter  30 value 70.811378
## iter  40 value 57.288783
## iter  50 value 49.843103
## iter  60 value 44.764321
## iter  70 value 41.814265
## iter  80 value 40.718875
## iter  90 value 40.008021
## iter 100 value 39.005373
## final  value 39.005373 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 602.977024 
## iter  10 value 81.916936
## iter  20 value 36.543117
## iter  30 value 14.093603
## iter  40 value 6.712179
## iter  50 value 5.977936
## iter  60 value 5.257628
## iter  70 value 4.455097
## iter  80 value 4.072794
## iter  90 value 3.949209
## iter 100 value 3.874716
## final  value 3.874716 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 905.459689 
## iter  10 value 87.775260
## iter  20 value 60.488821
## iter  30 value 39.967229
## iter  40 value 19.278600
## iter  50 value 15.364087
## iter  60 value 13.447160
## iter  70 value 12.251456
## iter  80 value 11.704857
## iter  90 value 11.192459
## iter 100 value 8.844190
## final  value 8.844190 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1538.129255 
## iter  10 value 244.909542
## iter  20 value 102.768325
## iter  30 value 68.507492
## iter  40 value 53.481096
## iter  50 value 45.260513
## iter  60 value 42.727321
## iter  70 value 41.885032
## iter  80 value 40.276497
## iter  90 value 39.248130
## iter 100 value 38.250390
## final  value 38.250390 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 876.765806 
## iter  10 value 102.243778
## iter  20 value 64.412121
## iter  30 value 33.012252
## iter  40 value 26.027412
## iter  50 value 23.179156
## iter  60 value 22.230955
## iter  70 value 21.745846
## iter  80 value 21.269525
## iter  90 value 20.974142
## iter 100 value 20.241918
## final  value 20.241918 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1229.268593 
## iter  10 value 235.769938
## iter  20 value 96.359450
## iter  30 value 54.534489
## iter  40 value 37.393512
## iter  50 value 28.346223
## iter  60 value 25.108182
## iter  70 value 21.645736
## iter  80 value 20.176711
## iter  90 value 18.175460
## iter 100 value 15.305250
## final  value 15.305250 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1373.947776 
## iter  10 value 116.118955
## iter  20 value 74.737077
## iter  30 value 55.286402
## iter  40 value 46.870614
## iter  50 value 40.350580
## iter  60 value 37.917823
## iter  70 value 36.022731
## iter  80 value 35.029902
## iter  90 value 33.693737
## iter 100 value 32.905702
## final  value 32.905702 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 958.467477 
## iter  10 value 106.815645
## iter  20 value 78.142167
## iter  30 value 59.075297
## iter  40 value 43.748628
## iter  50 value 39.528607
## iter  60 value 37.225546
## iter  70 value 35.098707
## iter  80 value 33.870376
## iter  90 value 32.758473
## iter 100 value 32.030941
## final  value 32.030941 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1020.085041 
## iter  10 value 120.312581
## iter  20 value 75.490103
## iter  30 value 50.262916
## iter  40 value 32.672800
## iter  50 value 26.408982
## iter  60 value 24.998748
## iter  70 value 24.578554
## iter  80 value 24.210696
## iter  90 value 24.041079
## iter 100 value 23.963108
## final  value 23.963108 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 787.670431 
## iter  10 value 99.626049
## iter  20 value 55.483483
## iter  30 value 38.777870
## iter  40 value 26.144862
## iter  50 value 17.342496
## iter  60 value 12.526539
## iter  70 value 11.222591
## iter  80 value 10.371436
## iter  90 value 9.383350
## iter 100 value 9.086345
## final  value 9.086345 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 816.459415 
## iter  10 value 128.363198
## iter  20 value 67.422538
## iter  30 value 43.336691
## iter  40 value 35.238251
## iter  50 value 29.203144
## iter  60 value 25.703217
## iter  70 value 22.351467
## iter  80 value 19.453724
## iter  90 value 17.275650
## iter 100 value 13.566179
## final  value 13.566179 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 669.572003 
## iter  10 value 95.439608
## iter  20 value 57.058007
## iter  30 value 37.675860
## iter  40 value 31.615621
## iter  50 value 29.183409
## iter  60 value 28.490178
## iter  70 value 27.930956
## iter  80 value 27.627639
## iter  90 value 27.405988
## iter 100 value 27.092092
## final  value 27.092092 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 937.947896 
## iter  10 value 155.433146
## iter  20 value 93.332267
## iter  30 value 69.692317
## iter  40 value 51.874773
## iter  50 value 44.911820
## iter  60 value 42.371358
## iter  70 value 40.751692
## iter  80 value 39.730398
## iter  90 value 38.931500
## iter 100 value 37.901559
## final  value 37.901559 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1362.681245 
## iter  10 value 136.645098
## iter  20 value 70.398410
## iter  30 value 49.035655
## iter  40 value 36.478044
## iter  50 value 27.411734
## iter  60 value 22.805904
## iter  70 value 21.659677
## iter  80 value 21.026508
## iter  90 value 20.417045
## iter 100 value 19.855364
## final  value 19.855364 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 801.712184 
## iter  10 value 106.890440
## iter  20 value 63.715239
## iter  30 value 42.284589
## iter  40 value 26.934272
## iter  50 value 21.469420
## iter  60 value 20.583393
## iter  70 value 20.259205
## iter  80 value 19.760058
## iter  90 value 19.437702
## iter 100 value 19.080187
## final  value 19.080187 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 958.893356 
## iter  10 value 117.739912
## iter  20 value 78.514909
## iter  30 value 55.458553
## iter  40 value 33.971222
## iter  50 value 19.004447
## iter  60 value 13.639367
## iter  70 value 11.556993
## iter  80 value 10.190688
## iter  90 value 9.234001
## iter 100 value 8.671773
## final  value 8.671773 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 684.924668 
## iter  10 value 123.810830
## iter  20 value 72.784528
## iter  30 value 44.544528
## iter  40 value 31.141320
## iter  50 value 24.483270
## iter  60 value 18.919017
## iter  70 value 16.427357
## iter  80 value 14.991818
## iter  90 value 14.601774
## iter 100 value 13.428682
## final  value 13.428682 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 752.596001 
## iter  10 value 119.070549
## iter  20 value 74.312208
## iter  30 value 47.310515
## iter  40 value 26.828916
## iter  50 value 15.387029
## iter  60 value 12.609255
## iter  70 value 11.196926
## iter  80 value 10.437621
## iter  90 value 9.919474
## iter 100 value 9.611893
## final  value 9.611893 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1178.233716 
## iter  10 value 135.212980
## iter  20 value 86.382072
## iter  30 value 65.024156
## iter  40 value 53.408779
## iter  50 value 46.324939
## iter  60 value 40.677681
## iter  70 value 37.644692
## iter  80 value 35.456070
## iter  90 value 32.397750
## iter 100 value 30.913243
## final  value 30.913243 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1171.507442 
## iter  10 value 112.400226
## iter  20 value 77.380539
## iter  30 value 54.309499
## iter  40 value 35.359586
## iter  50 value 29.231590
## iter  60 value 27.607850
## iter  70 value 24.954465
## iter  80 value 24.042056
## iter  90 value 23.278657
## iter 100 value 22.716072
## final  value 22.716072 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 986.382778 
## iter  10 value 124.118390
## iter  20 value 84.980406
## iter  30 value 59.662226
## iter  40 value 48.789330
## iter  50 value 41.205694
## iter  60 value 38.055544
## iter  70 value 35.583952
## iter  80 value 34.255801
## iter  90 value 33.545982
## iter 100 value 31.454750
## final  value 31.454750 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1035.654600 
## iter  10 value 136.872998
## iter  20 value 89.670162
## iter  30 value 69.339049
## iter  40 value 60.014844
## iter  50 value 55.864084
## iter  60 value 53.918867
## iter  70 value 52.268309
## iter  80 value 50.519994
## iter  90 value 49.972612
## iter 100 value 49.299920
## final  value 49.299920 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 998.883779 
## iter  10 value 122.692282
## iter  20 value 75.875781
## iter  30 value 40.014843
## iter  40 value 25.618163
## iter  50 value 20.952140
## iter  60 value 19.818228
## iter  70 value 18.712546
## iter  80 value 18.155922
## iter  90 value 17.779288
## iter 100 value 17.506133
## final  value 17.506133 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 870.909606 
## iter  10 value 126.546157
## iter  20 value 86.308122
## iter  30 value 64.365120
## iter  40 value 46.706123
## iter  50 value 36.964914
## iter  60 value 34.306044
## iter  70 value 32.338484
## iter  80 value 30.550328
## iter  90 value 29.727218
## iter 100 value 29.062878
## final  value 29.062878 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1094.977614 
## iter  10 value 246.996597
## iter  20 value 138.386626
## iter  30 value 110.186737
## iter  40 value 93.030385
## iter  50 value 81.882121
## iter  60 value 73.548204
## iter  70 value 66.941985
## iter  80 value 64.128388
## iter  90 value 61.621035
## iter 100 value 59.681211
## final  value 59.681211 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1343.003996 
## iter  10 value 105.950090
## iter  20 value 60.544584
## iter  30 value 38.174752
## iter  40 value 23.964806
## iter  50 value 15.568825
## iter  60 value 12.843077
## iter  70 value 11.031880
## iter  80 value 10.425515
## iter  90 value 9.984542
## iter 100 value 9.187526
## final  value 9.187526 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 989.188011 
## iter  10 value 125.778133
## iter  20 value 66.108669
## iter  30 value 43.936663
## iter  40 value 31.406872
## iter  50 value 28.493088
## iter  60 value 24.026612
## iter  70 value 21.834237
## iter  80 value 20.165868
## iter  90 value 19.666848
## iter 100 value 19.156032
## final  value 19.156032 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 660.795265 
## iter  10 value 128.244688
## iter  20 value 82.591528
## iter  30 value 54.438843
## iter  40 value 32.519308
## iter  50 value 24.797713
## iter  60 value 23.056237
## iter  70 value 22.185000
## iter  80 value 21.700963
## iter  90 value 20.903415
## iter 100 value 18.680382
## final  value 18.680382 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1377.816859 
## iter  10 value 98.815110
## iter  20 value 50.206089
## iter  30 value 26.937219
## iter  40 value 17.035395
## iter  50 value 15.216679
## iter  60 value 13.968427
## iter  70 value 12.923854
## iter  80 value 12.150796
## iter  90 value 10.426617
## iter 100 value 9.776004
## final  value 9.776004 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 685.616329 
## iter  10 value 96.856272
## iter  20 value 68.690326
## iter  30 value 37.684639
## iter  40 value 21.699825
## iter  50 value 18.122961
## iter  60 value 17.106474
## iter  70 value 16.393987
## iter  80 value 16.168592
## iter  90 value 15.991195
## iter 100 value 15.872940
## final  value 15.872940 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 2030.096629 
## iter  10 value 181.628074
## iter  20 value 94.709359
## iter  30 value 68.238904
## iter  40 value 50.700848
## iter  50 value 38.409176
## iter  60 value 32.264590
## iter  70 value 29.378007
## iter  80 value 27.795762
## iter  90 value 27.016899
## iter 100 value 25.701299
## final  value 25.701299 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 794.231695 
## iter  10 value 231.062594
## iter  20 value 122.550187
## iter  30 value 88.163432
## iter  40 value 65.920711
## iter  50 value 56.638999
## iter  60 value 50.889699
## iter  70 value 44.771752
## iter  80 value 42.624642
## iter  90 value 40.978539
## iter 100 value 35.273685
## final  value 35.273685 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 804.352282 
## iter  10 value 92.328850
## iter  20 value 54.440550
## iter  30 value 33.696359
## iter  40 value 26.385102
## iter  50 value 20.006807
## iter  60 value 17.215250
## iter  70 value 15.624967
## iter  80 value 14.230992
## iter  90 value 12.402213
## iter 100 value 11.422207
## final  value 11.422207 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 791.922526 
## iter  10 value 80.374726
## iter  20 value 45.850687
## iter  30 value 26.085471
## iter  40 value 20.313954
## iter  50 value 18.705951
## iter  60 value 17.292206
## iter  70 value 16.321032
## iter  80 value 15.723372
## iter  90 value 15.330824
## iter 100 value 14.697972
## final  value 14.697972 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1142.634006 
## iter  10 value 91.953014
## iter  20 value 57.735639
## iter  30 value 47.014894
## iter  40 value 34.769464
## iter  50 value 28.140591
## iter  60 value 20.303592
## iter  70 value 14.611589
## iter  80 value 13.746811
## iter  90 value 13.160948
## iter 100 value 12.596326
## final  value 12.596326 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 917.610518 
## iter  10 value 73.619242
## iter  20 value 42.993174
## iter  30 value 16.429382
## iter  40 value 10.205193
## iter  50 value 8.542214
## iter  60 value 7.570815
## iter  70 value 6.468226
## iter  80 value 4.403770
## iter  90 value 3.446107
## iter 100 value 2.936384
## final  value 2.936384 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1453.258778 
## iter  10 value 131.386731
## iter  20 value 81.209128
## iter  30 value 54.783246
## iter  40 value 49.362275
## iter  50 value 45.367482
## iter  60 value 43.559367
## iter  70 value 41.792054
## iter  80 value 40.515015
## iter  90 value 38.619932
## iter 100 value 37.717058
## final  value 37.717058 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1004.394543 
## iter  10 value 78.686051
## iter  20 value 40.177672
## iter  30 value 23.011903
## iter  40 value 18.559099
## iter  50 value 17.403519
## iter  60 value 16.899541
## iter  70 value 16.365314
## iter  80 value 11.306719
## iter  90 value 9.410705
## iter 100 value 7.292516
## final  value 7.292516 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 702.734526 
## iter  10 value 92.902451
## iter  20 value 61.293274
## iter  30 value 35.873042
## iter  40 value 27.889186
## iter  50 value 25.995341
## iter  60 value 19.966632
## iter  70 value 15.425380
## iter  80 value 13.849271
## iter  90 value 12.695455
## iter 100 value 11.947783
## final  value 11.947783 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1009.405591 
## iter  10 value 97.152003
## iter  20 value 59.597840
## iter  30 value 30.529994
## iter  40 value 24.966191
## iter  50 value 23.095315
## iter  60 value 22.247320
## iter  70 value 21.929189
## iter  80 value 20.309442
## iter  90 value 19.648262
## iter 100 value 17.842185
## final  value 17.842185 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 970.097635 
## iter  10 value 103.346826
## iter  20 value 53.905040
## iter  30 value 34.485881
## iter  40 value 28.085847
## iter  50 value 26.603093
## iter  60 value 25.824382
## iter  70 value 24.957690
## iter  80 value 24.590607
## iter  90 value 21.856082
## iter 100 value 20.611685
## final  value 20.611685 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 794.816883 
## iter  10 value 241.215163
## iter  20 value 163.696829
## iter  30 value 131.307213
## iter  40 value 93.707622
## iter  50 value 69.477284
## iter  60 value 60.459378
## iter  70 value 54.115491
## iter  80 value 45.898151
## iter  90 value 36.691753
## iter 100 value 32.984269
## final  value 32.984269 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1346.833580 
## iter  10 value 128.774481
## iter  20 value 84.764042
## iter  30 value 71.453646
## iter  40 value 53.230890
## iter  50 value 36.693667
## iter  60 value 31.452043
## iter  70 value 28.250114
## iter  80 value 26.767901
## iter  90 value 25.914109
## iter 100 value 25.215486
## final  value 25.215486 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1115.475846 
## iter  10 value 98.822667
## iter  20 value 51.664292
## iter  30 value 35.632678
## iter  40 value 28.008646
## iter  50 value 25.978566
## iter  60 value 23.793653
## iter  70 value 22.387126
## iter  80 value 21.201132
## iter  90 value 20.279423
## iter 100 value 19.692820
## final  value 19.692820 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 994.473465 
## iter  10 value 112.782070
## iter  20 value 81.537322
## iter  30 value 70.462529
## iter  40 value 64.043629
## iter  50 value 61.237440
## iter  60 value 59.409713
## iter  70 value 57.632632
## iter  80 value 56.859597
## iter  90 value 55.616237
## iter 100 value 51.534178
## final  value 51.534178 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1076.077352 
## iter  10 value 115.214574
## iter  20 value 59.734285
## iter  30 value 33.030560
## iter  40 value 21.204970
## iter  50 value 19.952812
## iter  60 value 17.996608
## iter  70 value 17.169366
## iter  80 value 16.667525
## iter  90 value 16.216140
## iter 100 value 15.799407
## final  value 15.799407 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 999.981494 
## iter  10 value 108.303090
## iter  20 value 71.324767
## iter  30 value 44.401046
## iter  40 value 36.433766
## iter  50 value 32.160041
## iter  60 value 28.179612
## iter  70 value 23.099466
## iter  80 value 20.666996
## iter  90 value 19.433885
## iter 100 value 18.812613
## final  value 18.812613 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1480.873616 
## iter  10 value 114.299332
## iter  20 value 61.501928
## iter  30 value 40.166622
## iter  40 value 23.155212
## iter  50 value 14.040416
## iter  60 value 10.274067
## iter  70 value 9.087053
## iter  80 value 8.416832
## iter  90 value 8.068070
## iter 100 value 7.791280
## final  value 7.791280 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1337.056573 
## iter  10 value 130.236983
## iter  20 value 77.089278
## iter  30 value 59.251346
## iter  40 value 41.761126
## iter  50 value 35.053963
## iter  60 value 32.163994
## iter  70 value 29.663763
## iter  80 value 28.100699
## iter  90 value 27.275945
## iter 100 value 26.375020
## final  value 26.375020 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 731.867930 
## iter  10 value 162.364573
## iter  20 value 95.716250
## iter  30 value 76.365062
## iter  40 value 70.082902
## iter  50 value 63.259417
## iter  60 value 57.683818
## iter  70 value 55.042665
## iter  80 value 52.581054
## iter  90 value 49.335049
## iter 100 value 43.920239
## final  value 43.920239 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 720.464240 
## iter  10 value 132.377738
## iter  20 value 77.475558
## iter  30 value 52.295722
## iter  40 value 32.233245
## iter  50 value 24.511211
## iter  60 value 22.130829
## iter  70 value 20.647207
## iter  80 value 19.625959
## iter  90 value 19.067073
## iter 100 value 18.455727
## final  value 18.455727 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 994.942367 
## iter  10 value 139.489939
## iter  20 value 82.726199
## iter  30 value 59.693095
## iter  40 value 53.251064
## iter  50 value 45.624392
## iter  60 value 42.372765
## iter  70 value 41.009867
## iter  80 value 36.092783
## iter  90 value 35.083461
## iter 100 value 34.651834
## final  value 34.651834 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 718.044060 
## iter  10 value 119.763882
## iter  20 value 75.606396
## iter  30 value 47.776392
## iter  40 value 32.611102
## iter  50 value 29.714777
## iter  60 value 24.801014
## iter  70 value 18.822698
## iter  80 value 16.879721
## iter  90 value 15.083869
## iter 100 value 13.614535
## final  value 13.614535 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 969.545312 
## iter  10 value 107.532859
## iter  20 value 68.292615
## iter  30 value 44.374401
## iter  40 value 30.050482
## iter  50 value 21.744737
## iter  60 value 19.347070
## iter  70 value 18.111552
## iter  80 value 17.709022
## iter  90 value 17.384284
## iter 100 value 17.149905
## final  value 17.149905 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1274.148852 
## iter  10 value 135.390362
## iter  20 value 84.345057
## iter  30 value 65.756191
## iter  40 value 52.829937
## iter  50 value 47.888379
## iter  60 value 46.182276
## iter  70 value 44.567339
## iter  80 value 41.479851
## iter  90 value 39.677854
## iter 100 value 39.011025
## final  value 39.011025 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 883.503467 
## iter  10 value 146.950569
## iter  20 value 99.800522
## iter  30 value 77.467913
## iter  40 value 63.686706
## iter  50 value 52.865081
## iter  60 value 44.112188
## iter  70 value 39.921784
## iter  80 value 38.444470
## iter  90 value 37.161190
## iter 100 value 35.454180
## final  value 35.454180 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 736.413398 
## iter  10 value 107.871913
## iter  20 value 60.341331
## iter  30 value 40.715343
## iter  40 value 30.688490
## iter  50 value 27.652550
## iter  60 value 24.209136
## iter  70 value 22.870356
## iter  80 value 21.623875
## iter  90 value 20.761122
## iter 100 value 20.214561
## final  value 20.214561 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1115.101698 
## iter  10 value 136.330693
## iter  20 value 66.339643
## iter  30 value 47.578957
## iter  40 value 35.231869
## iter  50 value 30.361403
## iter  60 value 28.322622
## iter  70 value 27.070896
## iter  80 value 25.676508
## iter  90 value 24.556233
## iter 100 value 24.224607
## final  value 24.224607 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1132.296477 
## iter  10 value 154.640044
## iter  20 value 111.436935
## iter  30 value 92.427753
## iter  40 value 82.368305
## iter  50 value 73.655168
## iter  60 value 68.595466
## iter  70 value 63.794059
## iter  80 value 58.899072
## iter  90 value 53.863184
## iter 100 value 51.444422
## final  value 51.444422 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 780.222311 
## iter  10 value 112.491795
## iter  20 value 69.607871
## iter  30 value 54.388149
## iter  40 value 44.480979
## iter  50 value 41.929979
## iter  60 value 39.745808
## iter  70 value 38.590747
## iter  80 value 35.217335
## iter  90 value 32.435333
## iter 100 value 30.653423
## final  value 30.653423 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 844.315464 
## iter  10 value 172.553177
## iter  20 value 118.216354
## iter  30 value 95.377488
## iter  40 value 80.766662
## iter  50 value 70.983436
## iter  60 value 65.080465
## iter  70 value 62.876391
## iter  80 value 59.982753
## iter  90 value 58.157213
## iter 100 value 55.853056
## final  value 55.853056 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 813.820556 
## iter  10 value 176.735531
## iter  20 value 94.199602
## iter  30 value 69.440571
## iter  40 value 47.249372
## iter  50 value 37.743345
## iter  60 value 33.703775
## iter  70 value 32.433814
## iter  80 value 30.604413
## iter  90 value 29.107477
## iter 100 value 27.689619
## final  value 27.689619 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1042.900626 
## iter  10 value 129.161115
## iter  20 value 83.981221
## iter  30 value 68.294333
## iter  40 value 60.041013
## iter  50 value 57.489980
## iter  60 value 56.473795
## iter  70 value 55.692239
## iter  80 value 55.172824
## iter  90 value 54.367818
## iter 100 value 53.966716
## final  value 53.966716 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 717.991380 
## iter  10 value 144.345574
## iter  20 value 72.514760
## iter  30 value 54.801269
## iter  40 value 36.916113
## iter  50 value 28.797515
## iter  60 value 27.355999
## iter  70 value 25.577596
## iter  80 value 22.358884
## iter  90 value 21.126822
## iter 100 value 20.440586
## final  value 20.440586 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1349.921403 
## iter  10 value 126.955048
## iter  20 value 83.703109
## iter  30 value 60.681808
## iter  40 value 41.861081
## iter  50 value 36.700924
## iter  60 value 33.942338
## iter  70 value 32.356780
## iter  80 value 31.072565
## iter  90 value 29.933116
## iter 100 value 29.027916
## final  value 29.027916 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 876.870335 
## iter  10 value 183.524266
## iter  20 value 92.973209
## iter  30 value 74.550930
## iter  40 value 49.847185
## iter  50 value 43.712979
## iter  60 value 41.022586
## iter  70 value 39.341253
## iter  80 value 37.962772
## iter  90 value 36.497213
## iter 100 value 33.009051
## final  value 33.009051 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 953.233513 
## iter  10 value 154.134730
## iter  20 value 110.545643
## iter  30 value 81.098197
## iter  40 value 69.256730
## iter  50 value 64.619137
## iter  60 value 62.122129
## iter  70 value 61.127790
## iter  80 value 60.658605
## iter  90 value 58.392526
## iter 100 value 56.852522
## final  value 56.852522 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1084.691054 
## iter  10 value 115.975012
## iter  20 value 70.020712
## iter  30 value 41.538210
## iter  40 value 34.011998
## iter  50 value 31.648410
## iter  60 value 30.150214
## iter  70 value 24.369037
## iter  80 value 20.893370
## iter  90 value 20.017412
## iter 100 value 19.318918
## final  value 19.318918 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1480.422654 
## iter  10 value 137.899044
## iter  20 value 86.987150
## iter  30 value 76.701365
## iter  40 value 69.950726
## iter  50 value 65.648188
## iter  60 value 64.202483
## iter  70 value 63.357573
## iter  80 value 62.570908
## iter  90 value 62.036155
## iter 100 value 61.622821
## final  value 61.622821 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 915.371359 
## iter  10 value 133.169843
## iter  20 value 91.644861
## iter  30 value 68.741231
## iter  40 value 50.511783
## iter  50 value 43.767245
## iter  60 value 39.279501
## iter  70 value 38.046900
## iter  80 value 34.691754
## iter  90 value 33.889729
## iter 100 value 32.925761
## final  value 32.925761 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 612.432276 
## iter  10 value 105.528719
## iter  20 value 69.748043
## iter  30 value 55.584598
## iter  40 value 50.840162
## iter  50 value 45.780670
## iter  60 value 43.038888
## iter  70 value 42.005894
## iter  80 value 41.038370
## iter  90 value 40.647454
## iter 100 value 39.820358
## final  value 39.820358 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 791.611289 
## iter  10 value 141.394431
## iter  20 value 91.315969
## iter  30 value 70.809137
## iter  40 value 58.569042
## iter  50 value 55.064937
## iter  60 value 53.330859
## iter  70 value 51.882163
## iter  80 value 50.825177
## iter  90 value 49.903481
## iter 100 value 49.514865
## final  value 49.514865 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 899.500261 
## iter  10 value 182.976118
## iter  20 value 115.182544
## iter  30 value 91.082302
## iter  40 value 77.788567
## iter  50 value 72.308987
## iter  60 value 68.466232
## iter  70 value 66.933042
## iter  80 value 65.355358
## iter  90 value 64.378141
## iter 100 value 63.912840
## final  value 63.912840 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 843.390142 
## iter  10 value 172.325322
## iter  20 value 91.032274
## iter  30 value 48.712986
## iter  40 value 32.612734
## iter  50 value 27.908284
## iter  60 value 25.412008
## iter  70 value 24.404378
## iter  80 value 23.122799
## iter  90 value 22.142991
## iter 100 value 20.705598
## final  value 20.705598 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 685.986206 
## iter  10 value 115.079235
## iter  20 value 71.307342
## iter  30 value 43.902246
## iter  40 value 26.022705
## iter  50 value 21.838969
## iter  60 value 21.111463
## iter  70 value 19.592066
## iter  80 value 18.145029
## iter  90 value 17.356991
## iter 100 value 16.395408
## final  value 16.395408 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1715.869016 
## iter  10 value 151.134019
## iter  20 value 67.149832
## iter  30 value 52.140637
## iter  40 value 36.552656
## iter  50 value 29.218936
## iter  60 value 26.755272
## iter  70 value 25.738501
## iter  80 value 23.842617
## iter  90 value 22.873563
## iter 100 value 21.011575
## final  value 21.011575 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 973.834124 
## iter  10 value 119.876193
## iter  20 value 57.229980
## iter  30 value 38.162901
## iter  40 value 26.032128
## iter  50 value 19.340770
## iter  60 value 15.087417
## iter  70 value 13.864697
## iter  80 value 13.248791
## iter  90 value 12.242137
## iter 100 value 11.357029
## final  value 11.357029 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 768.203382 
## iter  10 value 165.114855
## iter  20 value 114.814811
## iter  30 value 83.588648
## iter  40 value 62.654095
## iter  50 value 49.274570
## iter  60 value 43.799232
## iter  70 value 39.870257
## iter  80 value 38.364800
## iter  90 value 35.906100
## iter 100 value 33.104181
## final  value 33.104181 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1132.683659 
## iter  10 value 132.725889
## iter  20 value 83.648096
## iter  30 value 56.998306
## iter  40 value 42.095904
## iter  50 value 29.026993
## iter  60 value 21.883631
## iter  70 value 20.329321
## iter  80 value 19.657336
## iter  90 value 19.187257
## iter 100 value 18.503839
## final  value 18.503839 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1318.061451 
## iter  10 value 93.652280
## iter  20 value 48.605503
## iter  30 value 38.871825
## iter  40 value 32.330234
## iter  50 value 27.949793
## iter  60 value 26.285587
## iter  70 value 24.400598
## iter  80 value 23.174784
## iter  90 value 22.120761
## iter 100 value 21.413943
## final  value 21.413943 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 749.434344 
## iter  10 value 82.915603
## iter  20 value 44.335424
## iter  30 value 26.138166
## iter  40 value 22.557844
## iter  50 value 19.249625
## iter  60 value 18.685815
## iter  70 value 18.205269
## iter  80 value 17.835183
## iter  90 value 17.308113
## iter 100 value 17.077824
## final  value 17.077824 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 750.771420 
## iter  10 value 85.539636
## iter  20 value 50.774751
## iter  30 value 33.785440
## iter  40 value 25.276068
## iter  50 value 20.832315
## iter  60 value 19.575874
## iter  70 value 19.110008
## iter  80 value 18.746773
## iter  90 value 18.099872
## iter 100 value 17.485702
## final  value 17.485702 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 837.935724 
## iter  10 value 104.678860
## iter  20 value 70.331314
## iter  30 value 43.112192
## iter  40 value 36.010358
## iter  50 value 31.949608
## iter  60 value 28.512236
## iter  70 value 24.885878
## iter  80 value 21.490996
## iter  90 value 18.940057
## iter 100 value 16.899228
## final  value 16.899228 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 733.061612 
## iter  10 value 85.861931
## iter  20 value 40.896150
## iter  30 value 19.628036
## iter  40 value 8.301615
## iter  50 value 6.864015
## iter  60 value 5.777813
## iter  70 value 4.825645
## iter  80 value 4.044676
## iter  90 value 3.673086
## iter 100 value 3.329471
## final  value 3.329471 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 804.656004 
## iter  10 value 106.704614
## iter  20 value 78.745174
## iter  30 value 56.301049
## iter  40 value 50.987652
## iter  50 value 46.458547
## iter  60 value 37.736336
## iter  70 value 29.969998
## iter  80 value 23.620041
## iter  90 value 21.987130
## iter 100 value 20.360652
## final  value 20.360652 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1371.529806 
## iter  10 value 116.987375
## iter  20 value 69.404122
## iter  30 value 40.371780
## iter  40 value 30.982811
## iter  50 value 28.329761
## iter  60 value 25.356313
## iter  70 value 23.622488
## iter  80 value 21.953669
## iter  90 value 20.440078
## iter 100 value 17.596631
## final  value 17.596631 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1359.123893 
## iter  10 value 124.268957
## iter  20 value 79.845780
## iter  30 value 49.128022
## iter  40 value 32.450400
## iter  50 value 24.498899
## iter  60 value 22.178071
## iter  70 value 21.295359
## iter  80 value 19.491065
## iter  90 value 17.951807
## iter 100 value 16.619092
## final  value 16.619092 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 794.291917 
## iter  10 value 120.249749
## iter  20 value 65.003131
## iter  30 value 46.584542
## iter  40 value 26.183625
## iter  50 value 20.398880
## iter  60 value 17.536538
## iter  70 value 16.941450
## iter  80 value 16.230882
## iter  90 value 15.479427
## iter 100 value 14.761603
## final  value 14.761603 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1058.257891 
## iter  10 value 127.617488
## iter  20 value 61.804877
## iter  30 value 41.585867
## iter  40 value 26.872200
## iter  50 value 16.885778
## iter  60 value 11.471700
## iter  70 value 10.731752
## iter  80 value 10.250125
## iter  90 value 9.933431
## iter 100 value 9.684371
## final  value 9.684371 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1139.876386 
## iter  10 value 122.927424
## iter  20 value 66.026908
## iter  30 value 50.690813
## iter  40 value 36.567292
## iter  50 value 27.503259
## iter  60 value 20.296898
## iter  70 value 16.553138
## iter  80 value 15.287854
## iter  90 value 14.317042
## iter 100 value 13.958394
## final  value 13.958394 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1455.578618 
## iter  10 value 121.285943
## iter  20 value 84.571101
## iter  30 value 57.515920
## iter  40 value 36.479862
## iter  50 value 25.587390
## iter  60 value 22.521045
## iter  70 value 19.956009
## iter  80 value 17.860525
## iter  90 value 16.030331
## iter 100 value 14.512462
## final  value 14.512462 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1720.210392 
## iter  10 value 112.036497
## iter  20 value 62.702133
## iter  30 value 36.019148
## iter  40 value 20.402283
## iter  50 value 16.949159
## iter  60 value 16.005131
## iter  70 value 15.371601
## iter  80 value 14.986186
## iter  90 value 14.514148
## iter 100 value 14.146713
## final  value 14.146713 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 616.488965 
## iter  10 value 110.475508
## iter  20 value 83.164876
## iter  30 value 64.794793
## iter  40 value 53.181846
## iter  50 value 44.267023
## iter  60 value 36.318955
## iter  70 value 32.053900
## iter  80 value 28.964126
## iter  90 value 26.265002
## iter 100 value 22.889698
## final  value 22.889698 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 980.636493 
## iter  10 value 108.396637
## iter  20 value 63.636263
## iter  30 value 53.703427
## iter  40 value 48.536347
## iter  50 value 44.453316
## iter  60 value 41.075111
## iter  70 value 39.287900
## iter  80 value 37.943893
## iter  90 value 37.151393
## iter 100 value 36.148549
## final  value 36.148549 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1107.660839 
## iter  10 value 148.891918
## iter  20 value 68.998608
## iter  30 value 50.986302
## iter  40 value 41.609705
## iter  50 value 39.854296
## iter  60 value 34.440546
## iter  70 value 32.853409
## iter  80 value 31.583099
## iter  90 value 30.496940
## iter 100 value 29.647112
## final  value 29.647112 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1070.092134 
## iter  10 value 122.826802
## iter  20 value 88.389571
## iter  30 value 63.082926
## iter  40 value 49.943082
## iter  50 value 40.930214
## iter  60 value 37.794466
## iter  70 value 31.953884
## iter  80 value 28.089302
## iter  90 value 26.283530
## iter 100 value 25.836893
## final  value 25.836893 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1010.336075 
## iter  10 value 169.700812
## iter  20 value 114.672911
## iter  30 value 82.039452
## iter  40 value 70.709010
## iter  50 value 63.020304
## iter  60 value 59.701078
## iter  70 value 58.483494
## iter  80 value 58.013394
## iter  90 value 56.186726
## iter 100 value 55.622668
## final  value 55.622668 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 832.670736 
## iter  10 value 121.469375
## iter  20 value 79.659380
## iter  30 value 60.952257
## iter  40 value 52.372410
## iter  50 value 46.472346
## iter  60 value 42.957023
## iter  70 value 41.551713
## iter  80 value 40.928143
## iter  90 value 40.747873
## iter 100 value 40.532045
## final  value 40.532045 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 653.838913 
## iter  10 value 99.921634
## iter  20 value 65.453164
## iter  30 value 43.204345
## iter  40 value 32.100364
## iter  50 value 29.531816
## iter  60 value 28.204926
## iter  70 value 27.318692
## iter  80 value 23.756592
## iter  90 value 20.363891
## iter 100 value 16.683636
## final  value 16.683636 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 650.126645 
## iter  10 value 131.988070
## iter  20 value 72.301641
## iter  30 value 46.841182
## iter  40 value 37.543340
## iter  50 value 34.897372
## iter  60 value 31.752015
## iter  70 value 30.190234
## iter  80 value 28.964445
## iter  90 value 27.958008
## iter 100 value 26.923890
## final  value 26.923890 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 681.574119 
## iter  10 value 112.713191
## iter  20 value 67.027324
## iter  30 value 42.213726
## iter  40 value 30.351256
## iter  50 value 24.123072
## iter  60 value 22.041279
## iter  70 value 20.374832
## iter  80 value 19.686630
## iter  90 value 19.414022
## iter 100 value 19.067261
## final  value 19.067261 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 696.236415 
## iter  10 value 114.963697
## iter  20 value 73.508599
## iter  30 value 45.439138
## iter  40 value 36.813298
## iter  50 value 33.130904
## iter  60 value 31.226751
## iter  70 value 30.068554
## iter  80 value 29.082316
## iter  90 value 28.221512
## iter 100 value 27.372681
## final  value 27.372681 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1087.599283 
## iter  10 value 177.897319
## iter  20 value 101.864014
## iter  30 value 73.555360
## iter  40 value 64.557180
## iter  50 value 58.195372
## iter  60 value 54.470049
## iter  70 value 52.842115
## iter  80 value 52.053511
## iter  90 value 49.764329
## iter 100 value 49.141609
## final  value 49.141609 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1525.142569 
## iter  10 value 92.107558
## iter  20 value 64.730348
## iter  30 value 49.067027
## iter  40 value 32.857310
## iter  50 value 17.695426
## iter  60 value 14.387571
## iter  70 value 13.047550
## iter  80 value 12.209245
## iter  90 value 11.603386
## iter 100 value 11.246300
## final  value 11.246300 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 884.811872 
## iter  10 value 124.608178
## iter  20 value 80.172122
## iter  30 value 60.333323
## iter  40 value 43.215675
## iter  50 value 26.964605
## iter  60 value 22.165269
## iter  70 value 16.734103
## iter  80 value 14.657109
## iter  90 value 13.661108
## iter 100 value 12.943769
## final  value 12.943769 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1432.413150 
## iter  10 value 115.999090
## iter  20 value 64.448248
## iter  30 value 35.265756
## iter  40 value 26.566883
## iter  50 value 22.087279
## iter  60 value 20.390192
## iter  70 value 19.898979
## iter  80 value 19.641180
## iter  90 value 15.895854
## iter 100 value 14.862449
## final  value 14.862449 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1273.115969 
## iter  10 value 110.660713
## iter  20 value 70.557341
## iter  30 value 45.911926
## iter  40 value 35.307489
## iter  50 value 32.225676
## iter  60 value 30.903968
## iter  70 value 29.895179
## iter  80 value 28.976739
## iter  90 value 28.417181
## iter 100 value 27.773702
## final  value 27.773702 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1569.206773 
## iter  10 value 101.990725
## iter  20 value 61.625195
## iter  30 value 37.218704
## iter  40 value 30.166674
## iter  50 value 28.375105
## iter  60 value 26.581261
## iter  70 value 25.021100
## iter  80 value 22.855166
## iter  90 value 21.886564
## iter 100 value 21.105587
## final  value 21.105587 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 749.045361 
## iter  10 value 124.840275
## iter  20 value 71.304909
## iter  30 value 55.231656
## iter  40 value 40.310612
## iter  50 value 34.155307
## iter  60 value 32.616759
## iter  70 value 30.062087
## iter  80 value 29.495304
## iter  90 value 29.100205
## iter 100 value 28.935975
## final  value 28.935975 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1074.144073 
## iter  10 value 122.602564
## iter  20 value 79.023683
## iter  30 value 51.396323
## iter  40 value 38.737339
## iter  50 value 34.082656
## iter  60 value 29.541621
## iter  70 value 25.270752
## iter  80 value 23.957586
## iter  90 value 23.086291
## iter 100 value 22.625450
## final  value 22.625450 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 729.110515 
## iter  10 value 120.461690
## iter  20 value 68.128918
## iter  30 value 48.519165
## iter  40 value 40.737358
## iter  50 value 38.880868
## iter  60 value 36.491762
## iter  70 value 35.447867
## iter  80 value 34.561267
## iter  90 value 33.606879
## iter 100 value 30.233050
## final  value 30.233050 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1038.511571 
## iter  10 value 136.713879
## iter  20 value 78.740257
## iter  30 value 58.141732
## iter  40 value 46.261575
## iter  50 value 40.781748
## iter  60 value 36.520676
## iter  70 value 34.852384
## iter  80 value 33.759070
## iter  90 value 32.488329
## iter 100 value 28.999347
## final  value 28.999347 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 898.125716 
## iter  10 value 103.055729
## iter  20 value 62.446427
## iter  30 value 47.548532
## iter  40 value 41.102597
## iter  50 value 36.538746
## iter  60 value 33.654752
## iter  70 value 32.000368
## iter  80 value 31.410308
## iter  90 value 29.854406
## iter 100 value 26.499913
## final  value 26.499913 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 696.997977 
## iter  10 value 204.845486
## iter  20 value 84.713490
## iter  30 value 57.521070
## iter  40 value 46.184901
## iter  50 value 41.956975
## iter  60 value 39.634539
## iter  70 value 37.654539
## iter  80 value 34.544526
## iter  90 value 33.326978
## iter 100 value 31.376764
## final  value 31.376764 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 853.786188 
## iter  10 value 136.069554
## iter  20 value 80.937084
## iter  30 value 59.088447
## iter  40 value 45.390834
## iter  50 value 37.643482
## iter  60 value 34.271448
## iter  70 value 32.083538
## iter  80 value 30.121449
## iter  90 value 29.004795
## iter 100 value 28.528909
## final  value 28.528909 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 724.556141 
## iter  10 value 97.220745
## iter  20 value 66.112889
## iter  30 value 44.708383
## iter  40 value 30.161878
## iter  50 value 24.709908
## iter  60 value 21.915945
## iter  70 value 20.446111
## iter  80 value 19.999900
## iter  90 value 19.688376
## iter 100 value 19.235186
## final  value 19.235186 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 722.650904 
## iter  10 value 131.248207
## iter  20 value 72.174544
## iter  30 value 33.414209
## iter  40 value 15.338560
## iter  50 value 11.434316
## iter  60 value 10.739429
## iter  70 value 10.199042
## iter  80 value 8.985894
## iter  90 value 8.742954
## iter 100 value 8.600469
## final  value 8.600469 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 929.604183 
## iter  10 value 132.715706
## iter  20 value 83.238226
## iter  30 value 37.807988
## iter  40 value 22.889731
## iter  50 value 19.862385
## iter  60 value 17.115762
## iter  70 value 15.848101
## iter  80 value 14.849491
## iter  90 value 13.886712
## iter 100 value 12.875573
## final  value 12.875573 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1156.311483 
## iter  10 value 110.665917
## iter  20 value 70.741795
## iter  30 value 49.154899
## iter  40 value 31.097041
## iter  50 value 24.443584
## iter  60 value 23.175117
## iter  70 value 22.696506
## iter  80 value 19.327775
## iter  90 value 17.517186
## iter 100 value 15.730071
## final  value 15.730071 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 688.586228 
## iter  10 value 113.522736
## iter  20 value 66.955265
## iter  30 value 35.537672
## iter  40 value 26.859962
## iter  50 value 24.931386
## iter  60 value 22.850959
## iter  70 value 20.674771
## iter  80 value 16.959991
## iter  90 value 14.035427
## iter 100 value 10.824480
## final  value 10.824480 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1287.437465 
## iter  10 value 152.398223
## iter  20 value 81.182216
## iter  30 value 52.337860
## iter  40 value 33.207766
## iter  50 value 22.078765
## iter  60 value 18.589640
## iter  70 value 16.993234
## iter  80 value 15.596991
## iter  90 value 13.967097
## iter 100 value 13.061958
## final  value 13.061958 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 727.766343 
## iter  10 value 81.888791
## iter  20 value 45.675003
## iter  30 value 28.756903
## iter  40 value 21.464436
## iter  50 value 18.713100
## iter  60 value 17.804659
## iter  70 value 16.428548
## iter  80 value 15.269225
## iter  90 value 14.483359
## iter 100 value 13.068988
## final  value 13.068988 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1034.406938 
## iter  10 value 111.843864
## iter  20 value 64.512884
## iter  30 value 48.180492
## iter  40 value 37.578619
## iter  50 value 32.076349
## iter  60 value 29.571327
## iter  70 value 28.562348
## iter  80 value 28.105055
## iter  90 value 27.762828
## iter 100 value 27.246229
## final  value 27.246229 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1239.788513 
## iter  10 value 101.131070
## iter  20 value 46.288456
## iter  30 value 28.650358
## iter  40 value 23.407402
## iter  50 value 19.877366
## iter  60 value 17.972947
## iter  70 value 15.854730
## iter  80 value 14.736527
## iter  90 value 13.980372
## iter 100 value 13.582358
## final  value 13.582358 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 948.519969 
## iter  10 value 89.414179
## iter  20 value 50.407962
## iter  30 value 27.445512
## iter  40 value 17.340545
## iter  50 value 13.630646
## iter  60 value 12.967318
## iter  70 value 12.466504
## iter  80 value 12.181103
## iter  90 value 11.710975
## iter 100 value 11.422071
## final  value 11.422071 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 997.390482 
## iter  10 value 107.803848
## iter  20 value 61.225402
## iter  30 value 35.994448
## iter  40 value 15.930021
## iter  50 value 7.796505
## iter  60 value 4.917565
## iter  70 value 4.009718
## iter  80 value 3.422907
## iter  90 value 3.078035
## iter 100 value 2.909469
## final  value 2.909469 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 892.576310 
## iter  10 value 105.531565
## iter  20 value 71.842812
## iter  30 value 49.634674
## iter  40 value 34.557683
## iter  50 value 23.024306
## iter  60 value 18.712422
## iter  70 value 17.600765
## iter  80 value 16.783457
## iter  90 value 16.280489
## iter 100 value 15.916382
## final  value 15.916382 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 963.716384 
## iter  10 value 98.889697
## iter  20 value 59.897267
## iter  30 value 47.360335
## iter  40 value 34.322169
## iter  50 value 22.352148
## iter  60 value 19.901126
## iter  70 value 18.412202
## iter  80 value 17.862066
## iter  90 value 16.154628
## iter 100 value 14.982026
## final  value 14.982026 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 668.164548 
## iter  10 value 106.694854
## iter  20 value 68.686723
## iter  30 value 37.599516
## iter  40 value 27.389322
## iter  50 value 23.386028
## iter  60 value 21.976666
## iter  70 value 19.318741
## iter  80 value 17.563898
## iter  90 value 16.844834
## iter 100 value 16.417868
## final  value 16.417868 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 610.588377 
## iter  10 value 103.066189
## iter  20 value 54.860827
## iter  30 value 33.723727
## iter  40 value 23.665686
## iter  50 value 19.443873
## iter  60 value 16.503197
## iter  70 value 13.619768
## iter  80 value 12.608799
## iter  90 value 11.960736
## iter 100 value 10.123797
## final  value 10.123797 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 797.042201 
## iter  10 value 120.956297
## iter  20 value 81.773114
## iter  30 value 50.995370
## iter  40 value 35.336489
## iter  50 value 30.276480
## iter  60 value 27.873624
## iter  70 value 26.235917
## iter  80 value 25.199752
## iter  90 value 20.967634
## iter 100 value 19.493573
## final  value 19.493573 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 912.850286 
## iter  10 value 120.340255
## iter  20 value 72.183522
## iter  30 value 39.262645
## iter  40 value 33.010396
## iter  50 value 30.084487
## iter  60 value 28.878122
## iter  70 value 28.666724
## iter  80 value 28.438601
## iter  90 value 28.302606
## iter 100 value 28.238110
## final  value 28.238110 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1510.936265 
## iter  10 value 121.093622
## iter  20 value 86.204212
## iter  30 value 65.299265
## iter  40 value 53.869891
## iter  50 value 42.818379
## iter  60 value 36.356852
## iter  70 value 31.383731
## iter  80 value 28.083352
## iter  90 value 26.787592
## iter 100 value 24.770384
## final  value 24.770384 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 855.364026 
## iter  10 value 150.790808
## iter  20 value 89.946132
## iter  30 value 68.797589
## iter  40 value 53.096679
## iter  50 value 46.800157
## iter  60 value 43.753812
## iter  70 value 41.133787
## iter  80 value 39.503124
## iter  90 value 37.637370
## iter 100 value 36.755099
## final  value 36.755099 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 750.728629 
## iter  10 value 151.310473
## iter  20 value 105.000577
## iter  30 value 88.771554
## iter  40 value 74.780331
## iter  50 value 66.098472
## iter  60 value 60.735240
## iter  70 value 56.058464
## iter  80 value 51.632791
## iter  90 value 47.940664
## iter 100 value 46.060296
## final  value 46.060296 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 936.917719 
## iter  10 value 113.968599
## iter  20 value 77.768396
## iter  30 value 53.772356
## iter  40 value 39.384883
## iter  50 value 29.393870
## iter  60 value 22.593457
## iter  70 value 19.893682
## iter  80 value 18.949812
## iter  90 value 18.365404
## iter 100 value 17.874060
## final  value 17.874060 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1114.591910 
## iter  10 value 152.868193
## iter  20 value 67.299463
## iter  30 value 44.483335
## iter  40 value 29.504361
## iter  50 value 24.055573
## iter  60 value 22.024497
## iter  70 value 21.440002
## iter  80 value 20.757315
## iter  90 value 18.939161
## iter 100 value 18.485217
## final  value 18.485217 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 780.230103 
## iter  10 value 127.682431
## iter  20 value 71.272620
## iter  30 value 48.893409
## iter  40 value 37.393835
## iter  50 value 33.601114
## iter  60 value 31.126520
## iter  70 value 29.559986
## iter  80 value 24.421898
## iter  90 value 21.743054
## iter 100 value 20.754517
## final  value 20.754517 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1515.431397 
## iter  10 value 130.884565
## iter  20 value 91.302021
## iter  30 value 75.887223
## iter  40 value 62.412428
## iter  50 value 54.511673
## iter  60 value 50.695558
## iter  70 value 48.857288
## iter  80 value 47.419206
## iter  90 value 46.848443
## iter 100 value 46.260494
## final  value 46.260494 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1008.111161 
## iter  10 value 96.152441
## iter  20 value 40.661552
## iter  30 value 20.901792
## iter  40 value 14.622708
## iter  50 value 12.686072
## iter  60 value 11.993068
## iter  70 value 11.528511
## iter  80 value 11.221383
## iter  90 value 10.870953
## iter 100 value 10.403879
## final  value 10.403879 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1108.390293 
## iter  10 value 136.791550
## iter  20 value 67.226756
## iter  30 value 58.526213
## iter  40 value 50.366540
## iter  50 value 46.516933
## iter  60 value 43.343318
## iter  70 value 40.051354
## iter  80 value 35.603422
## iter  90 value 32.038444
## iter 100 value 30.213831
## final  value 30.213831 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 939.128006 
## iter  10 value 93.527310
## iter  20 value 51.081082
## iter  30 value 44.601290
## iter  40 value 40.155787
## iter  50 value 35.977829
## iter  60 value 31.471635
## iter  70 value 30.234153
## iter  80 value 29.223352
## iter  90 value 27.323845
## iter 100 value 25.729100
## final  value 25.729100 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1078.052192 
## iter  10 value 106.541726
## iter  20 value 74.800148
## iter  30 value 61.206327
## iter  40 value 52.359180
## iter  50 value 47.897122
## iter  60 value 46.598965
## iter  70 value 44.842714
## iter  80 value 43.310605
## iter  90 value 43.096372
## iter 100 value 42.243832
## final  value 42.243832 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1135.595143 
## iter  10 value 97.835319
## iter  20 value 57.381881
## iter  30 value 37.267519
## iter  40 value 31.573768
## iter  50 value 27.649919
## iter  60 value 26.807899
## iter  70 value 25.953230
## iter  80 value 25.282367
## iter  90 value 24.264768
## iter 100 value 23.736681
## final  value 23.736681 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 923.130964 
## iter  10 value 111.012916
## iter  20 value 75.848854
## iter  30 value 54.567402
## iter  40 value 44.212329
## iter  50 value 40.690814
## iter  60 value 37.439021
## iter  70 value 35.648045
## iter  80 value 34.268674
## iter  90 value 32.301537
## iter 100 value 31.280747
## final  value 31.280747 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 667.413986 
## iter  10 value 78.548647
## iter  20 value 41.075636
## iter  30 value 23.502743
## iter  40 value 13.822720
## iter  50 value 9.484292
## iter  60 value 8.302038
## iter  70 value 7.807672
## iter  80 value 7.363621
## iter  90 value 7.199705
## iter 100 value 7.086166
## final  value 7.086166 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1204.935713 
## iter  10 value 96.001703
## iter  20 value 58.082517
## iter  30 value 36.127736
## iter  40 value 21.724880
## iter  50 value 18.098742
## iter  60 value 16.334650
## iter  70 value 15.092416
## iter  80 value 14.372863
## iter  90 value 13.895955
## iter 100 value 13.512741
## final  value 13.512741 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 862.490912 
## iter  10 value 81.052163
## iter  20 value 62.670007
## iter  30 value 50.845399
## iter  40 value 40.558804
## iter  50 value 32.919504
## iter  60 value 26.265535
## iter  70 value 24.109629
## iter  80 value 22.735471
## iter  90 value 21.736606
## iter 100 value 20.116441
## final  value 20.116441 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 643.919011 
## iter  10 value 122.155329
## iter  20 value 62.678522
## iter  30 value 33.859162
## iter  40 value 21.230155
## iter  50 value 17.471724
## iter  60 value 14.874140
## iter  70 value 12.241103
## iter  80 value 11.044352
## iter  90 value 10.272909
## iter 100 value 9.388053
## final  value 9.388053 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 948.222265 
## iter  10 value 111.493613
## iter  20 value 57.271408
## iter  30 value 40.915141
## iter  40 value 32.629565
## iter  50 value 24.378764
## iter  60 value 16.441118
## iter  70 value 13.134538
## iter  80 value 12.044945
## iter  90 value 11.130367
## iter 100 value 10.491701
## final  value 10.491701 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 913.968713 
## iter  10 value 142.426030
## iter  20 value 86.546005
## iter  30 value 49.509434
## iter  40 value 27.570277
## iter  50 value 20.834858
## iter  60 value 19.442306
## iter  70 value 18.324156
## iter  80 value 17.646262
## iter  90 value 17.105379
## iter 100 value 16.538233
## final  value 16.538233 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 779.211598 
## iter  10 value 88.103385
## iter  20 value 55.320229
## iter  30 value 38.846420
## iter  40 value 30.123373
## iter  50 value 22.086522
## iter  60 value 18.844106
## iter  70 value 17.048241
## iter  80 value 15.926693
## iter  90 value 15.317315
## iter 100 value 14.319306
## final  value 14.319306 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 995.158472 
## iter  10 value 89.270473
## iter  20 value 49.688178
## iter  30 value 30.938962
## iter  40 value 21.741238
## iter  50 value 17.003384
## iter  60 value 12.647884
## iter  70 value 10.859148
## iter  80 value 9.673285
## iter  90 value 9.076578
## iter 100 value 8.664871
## final  value 8.664871 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 885.467299 
## iter  10 value 103.017255
## iter  20 value 57.832779
## iter  30 value 33.891073
## iter  40 value 25.215178
## iter  50 value 22.947245
## iter  60 value 22.241070
## iter  70 value 21.306155
## iter  80 value 19.949347
## iter  90 value 19.194567
## iter 100 value 18.212934
## final  value 18.212934 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 706.150677 
## iter  10 value 87.695295
## iter  20 value 58.281906
## iter  30 value 31.151307
## iter  40 value 26.132744
## iter  50 value 21.969520
## iter  60 value 20.059689
## iter  70 value 19.254434
## iter  80 value 18.944450
## iter  90 value 18.808251
## iter 100 value 18.732225
## final  value 18.732225 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 757.437885 
## iter  10 value 113.395104
## iter  20 value 62.828076
## iter  30 value 37.341518
## iter  40 value 22.578811
## iter  50 value 14.984327
## iter  60 value 13.653442
## iter  70 value 11.871841
## iter  80 value 10.889926
## iter  90 value 10.135877
## iter 100 value 8.255826
## final  value 8.255826 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1585.148710 
## iter  10 value 127.616696
## iter  20 value 90.115222
## iter  30 value 75.996128
## iter  40 value 57.682928
## iter  50 value 32.675457
## iter  60 value 24.027085
## iter  70 value 20.018827
## iter  80 value 18.343391
## iter  90 value 17.533152
## iter 100 value 16.696660
## final  value 16.696660 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1157.164870 
## iter  10 value 110.573224
## iter  20 value 63.356354
## iter  30 value 38.343576
## iter  40 value 29.822137
## iter  50 value 26.535151
## iter  60 value 25.581674
## iter  70 value 24.580505
## iter  80 value 23.843987
## iter  90 value 23.241075
## iter 100 value 22.791695
## final  value 22.791695 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1084.526944 
## iter  10 value 127.214003
## iter  20 value 90.898793
## iter  30 value 67.703175
## iter  40 value 49.420160
## iter  50 value 39.770405
## iter  60 value 36.888431
## iter  70 value 34.580019
## iter  80 value 33.713377
## iter  90 value 33.111045
## iter 100 value 32.318723
## final  value 32.318723 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 665.639222 
## iter  10 value 134.456155
## iter  20 value 91.788850
## iter  30 value 75.098583
## iter  40 value 68.141223
## iter  50 value 57.486471
## iter  60 value 52.040760
## iter  70 value 50.690541
## iter  80 value 48.663508
## iter  90 value 45.922657
## iter 100 value 44.815995
## final  value 44.815995 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 729.271194 
## iter  10 value 131.337393
## iter  20 value 82.306425
## iter  30 value 47.683303
## iter  40 value 34.299458
## iter  50 value 32.521808
## iter  60 value 30.882733
## iter  70 value 26.898135
## iter  80 value 24.735155
## iter  90 value 23.951567
## iter 100 value 23.288320
## final  value 23.288320 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 743.321953 
## iter  10 value 156.866550
## iter  20 value 85.813922
## iter  30 value 52.634102
## iter  40 value 37.927402
## iter  50 value 26.141612
## iter  60 value 20.326364
## iter  70 value 18.321162
## iter  80 value 16.061087
## iter  90 value 14.385197
## iter 100 value 13.219578
## final  value 13.219578 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1426.599463 
## iter  10 value 175.870833
## iter  20 value 109.659830
## iter  30 value 81.095574
## iter  40 value 69.605113
## iter  50 value 64.774765
## iter  60 value 59.124263
## iter  70 value 51.516351
## iter  80 value 49.647411
## iter  90 value 48.776320
## iter 100 value 48.437156
## final  value 48.437156 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1164.532556 
## iter  10 value 101.497182
## iter  20 value 62.378526
## iter  30 value 37.135040
## iter  40 value 20.823781
## iter  50 value 16.001275
## iter  60 value 13.459576
## iter  70 value 11.647172
## iter  80 value 10.094377
## iter  90 value 9.094751
## iter 100 value 7.463370
## final  value 7.463370 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 747.068277 
## iter  10 value 122.038151
## iter  20 value 69.893956
## iter  30 value 42.198125
## iter  40 value 22.440635
## iter  50 value 15.532606
## iter  60 value 13.990352
## iter  70 value 13.602359
## iter  80 value 13.407468
## iter  90 value 13.243972
## iter 100 value 12.957137
## final  value 12.957137 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1439.373488 
## iter  10 value 120.869695
## iter  20 value 73.012444
## iter  30 value 48.867673
## iter  40 value 31.528316
## iter  50 value 20.449706
## iter  60 value 15.849922
## iter  70 value 13.712143
## iter  80 value 12.846147
## iter  90 value 12.307429
## iter 100 value 11.624237
## final  value 11.624237 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 810.198878 
## iter  10 value 111.614347
## iter  20 value 73.606754
## iter  30 value 42.616721
## iter  40 value 28.023017
## iter  50 value 23.645841
## iter  60 value 21.138176
## iter  70 value 18.561554
## iter  80 value 16.908797
## iter  90 value 12.043844
## iter 100 value 11.356238
## final  value 11.356238 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 929.342127 
## iter  10 value 231.628582
## iter  20 value 145.886230
## iter  30 value 103.311282
## iter  40 value 80.200437
## iter  50 value 67.287448
## iter  60 value 57.834562
## iter  70 value 51.291319
## iter  80 value 48.796893
## iter  90 value 46.301744
## iter 100 value 43.309415
## final  value 43.309415 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 803.203668 
## iter  10 value 140.468958
## iter  20 value 106.459005
## iter  30 value 80.953560
## iter  40 value 64.230508
## iter  50 value 56.087502
## iter  60 value 49.901381
## iter  70 value 42.490812
## iter  80 value 40.384516
## iter  90 value 39.195897
## iter 100 value 38.773849
## final  value 38.773849 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 915.344937 
## iter  10 value 140.760314
## iter  20 value 102.347888
## iter  30 value 67.224318
## iter  40 value 52.135375
## iter  50 value 34.908184
## iter  60 value 28.129830
## iter  70 value 26.433109
## iter  80 value 23.193932
## iter  90 value 22.261079
## iter 100 value 21.608107
## final  value 21.608107 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 674.909587 
## iter  10 value 151.118601
## iter  20 value 81.975875
## iter  30 value 51.807425
## iter  40 value 42.732641
## iter  50 value 36.725790
## iter  60 value 31.570441
## iter  70 value 27.959704
## iter  80 value 26.348078
## iter  90 value 25.415006
## iter 100 value 24.665755
## final  value 24.665755 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1143.054866 
## iter  10 value 154.988946
## iter  20 value 83.377842
## iter  30 value 57.643809
## iter  40 value 38.562837
## iter  50 value 34.141108
## iter  60 value 30.474764
## iter  70 value 28.498638
## iter  80 value 27.696195
## iter  90 value 27.133768
## iter 100 value 25.615499
## final  value 25.615499 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1038.101159 
## iter  10 value 157.830366
## iter  20 value 89.791628
## iter  30 value 74.148015
## iter  40 value 67.829965
## iter  50 value 58.118114
## iter  60 value 49.537422
## iter  70 value 36.373264
## iter  80 value 27.412029
## iter  90 value 24.421534
## iter 100 value 21.568804
## final  value 21.568804 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1870.392804 
## iter  10 value 111.742293
## iter  20 value 78.416774
## iter  30 value 37.413716
## iter  40 value 22.419051
## iter  50 value 18.530898
## iter  60 value 16.195441
## iter  70 value 14.857734
## iter  80 value 14.020504
## iter  90 value 13.276830
## iter 100 value 12.652003
## final  value 12.652003 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1359.567476 
## iter  10 value 184.724612
## iter  20 value 105.368775
## iter  30 value 75.651926
## iter  40 value 62.774582
## iter  50 value 54.409675
## iter  60 value 49.585730
## iter  70 value 47.305445
## iter  80 value 45.098368
## iter  90 value 43.412867
## iter 100 value 42.424167
## final  value 42.424167 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 615.957742 
## iter  10 value 123.537621
## iter  20 value 85.126385
## iter  30 value 65.341559
## iter  40 value 58.134595
## iter  50 value 53.035701
## iter  60 value 47.527380
## iter  70 value 44.241125
## iter  80 value 42.273946
## iter  90 value 39.356986
## iter 100 value 38.628085
## final  value 38.628085 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1277.697201 
## iter  10 value 116.587728
## iter  20 value 67.518117
## iter  30 value 45.466968
## iter  40 value 28.056694
## iter  50 value 20.773892
## iter  60 value 15.674598
## iter  70 value 14.318682
## iter  80 value 13.387719
## iter  90 value 12.738792
## iter 100 value 11.998247
## final  value 11.998247 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1271.590418 
## iter  10 value 157.555430
## iter  20 value 85.945015
## iter  30 value 68.739034
## iter  40 value 59.911231
## iter  50 value 57.654448
## iter  60 value 55.424772
## iter  70 value 53.490276
## iter  80 value 51.088083
## iter  90 value 46.502533
## iter 100 value 43.579574
## final  value 43.579574 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 774.240900 
## iter  10 value 107.269381
## iter  20 value 63.069436
## iter  30 value 37.196042
## iter  40 value 26.629742
## iter  50 value 21.490513
## iter  60 value 19.971261
## iter  70 value 18.310898
## iter  80 value 17.444658
## iter  90 value 15.884510
## iter 100 value 13.262066
## final  value 13.262066 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 653.605989 
## iter  10 value 90.714175
## iter  20 value 59.783598
## iter  30 value 32.952253
## iter  40 value 23.783633
## iter  50 value 22.034944
## iter  60 value 21.363951
## iter  70 value 20.921917
## iter  80 value 20.640851
## iter  90 value 20.505943
## iter 100 value 20.452005
## final  value 20.452005 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1664.401219 
## iter  10 value 229.251579
## iter  20 value 132.552491
## iter  30 value 85.149663
## iter  40 value 71.661893
## iter  50 value 61.482888
## iter  60 value 54.857272
## iter  70 value 50.697813
## iter  80 value 48.640810
## iter  90 value 47.051366
## iter 100 value 46.569148
## final  value 46.569148 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 873.281962 
## iter  10 value 127.996767
## iter  20 value 79.572446
## iter  30 value 58.201108
## iter  40 value 45.935450
## iter  50 value 41.766729
## iter  60 value 38.529226
## iter  70 value 36.154743
## iter  80 value 34.992412
## iter  90 value 34.163193
## iter 100 value 32.597669
## final  value 32.597669 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 682.284859 
## iter  10 value 122.048590
## iter  20 value 82.289697
## iter  30 value 60.483383
## iter  40 value 46.008179
## iter  50 value 38.958008
## iter  60 value 36.497061
## iter  70 value 34.488804
## iter  80 value 32.984849
## iter  90 value 31.854973
## iter 100 value 30.606523
## final  value 30.606523 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1006.224055 
## iter  10 value 149.227806
## iter  20 value 79.727257
## iter  30 value 63.847386
## iter  40 value 57.779368
## iter  50 value 54.845354
## iter  60 value 52.662973
## iter  70 value 50.913059
## iter  80 value 49.620423
## iter  90 value 47.543424
## iter 100 value 46.841143
## final  value 46.841143 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 903.803449 
## iter  10 value 129.652308
## iter  20 value 86.813968
## iter  30 value 72.907355
## iter  40 value 66.520217
## iter  50 value 62.480161
## iter  60 value 59.306847
## iter  70 value 56.884145
## iter  80 value 55.531989
## iter  90 value 54.226416
## iter 100 value 53.082079
## final  value 53.082079 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 894.168680 
## iter  10 value 112.414883
## iter  20 value 62.849938
## iter  30 value 32.082062
## iter  40 value 15.055072
## iter  50 value 10.282712
## iter  60 value 8.784533
## iter  70 value 8.156645
## iter  80 value 7.735823
## iter  90 value 7.056352
## iter 100 value 6.662838
## final  value 6.662838 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1232.675698 
## iter  10 value 146.780932
## iter  20 value 97.282190
## iter  30 value 71.442856
## iter  40 value 54.406879
## iter  50 value 45.052354
## iter  60 value 40.150246
## iter  70 value 38.086895
## iter  80 value 36.883451
## iter  90 value 35.344982
## iter 100 value 34.536042
## final  value 34.536042 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 788.185431 
## iter  10 value 92.202446
## iter  20 value 45.770256
## iter  30 value 22.055105
## iter  40 value 14.463348
## iter  50 value 12.264601
## iter  60 value 11.492775
## iter  70 value 10.360196
## iter  80 value 9.593864
## iter  90 value 8.501445
## iter 100 value 8.004739
## final  value 8.004739 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 967.774420 
## iter  10 value 154.154151
## iter  20 value 75.140227
## iter  30 value 47.632284
## iter  40 value 37.904909
## iter  50 value 34.243007
## iter  60 value 32.580468
## iter  70 value 31.142801
## iter  80 value 30.046356
## iter  90 value 29.096072
## iter 100 value 28.518464
## final  value 28.518464 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 860.016269 
## iter  10 value 145.679755
## iter  20 value 77.670248
## iter  30 value 45.697948
## iter  40 value 26.523448
## iter  50 value 20.283752
## iter  60 value 18.085218
## iter  70 value 16.554127
## iter  80 value 15.896341
## iter  90 value 15.127953
## iter 100 value 10.495466
## final  value 10.495466 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 698.936853 
## iter  10 value 100.968499
## iter  20 value 62.407217
## iter  30 value 39.872716
## iter  40 value 31.857413
## iter  50 value 29.120518
## iter  60 value 27.648080
## iter  70 value 26.833835
## iter  80 value 24.979109
## iter  90 value 23.354350
## iter 100 value 22.691058
## final  value 22.691058 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 851.828295 
## iter  10 value 150.498814
## iter  20 value 83.622196
## iter  30 value 56.326510
## iter  40 value 46.106244
## iter  50 value 41.944302
## iter  60 value 39.895324
## iter  70 value 38.688031
## iter  80 value 37.707072
## iter  90 value 37.108843
## iter 100 value 36.763477
## final  value 36.763477 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 2092.385443 
## iter  10 value 167.558886
## iter  20 value 100.879300
## iter  30 value 76.360467
## iter  40 value 58.027397
## iter  50 value 42.177186
## iter  60 value 31.221391
## iter  70 value 26.636117
## iter  80 value 25.037079
## iter  90 value 23.215281
## iter 100 value 22.446532
## final  value 22.446532 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1228.924987 
## iter  10 value 85.178115
## iter  20 value 49.602292
## iter  30 value 29.146849
## iter  40 value 20.190281
## iter  50 value 16.460944
## iter  60 value 8.120939
## iter  70 value 6.770546
## iter  80 value 6.370322
## iter  90 value 5.887599
## iter 100 value 5.651198
## final  value 5.651198 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 869.328511 
## iter  10 value 109.963543
## iter  20 value 79.203970
## iter  30 value 54.582756
## iter  40 value 33.993258
## iter  50 value 19.745597
## iter  60 value 15.601872
## iter  70 value 14.672381
## iter  80 value 13.884752
## iter  90 value 12.302464
## iter 100 value 11.286197
## final  value 11.286197 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 789.998014 
## iter  10 value 89.444986
## iter  20 value 49.570088
## iter  30 value 35.389270
## iter  40 value 26.113648
## iter  50 value 20.708830
## iter  60 value 18.942385
## iter  70 value 17.800696
## iter  80 value 17.385608
## iter  90 value 16.649574
## iter 100 value 15.744861
## final  value 15.744861 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 635.278448 
## iter  10 value 120.882600
## iter  20 value 71.655607
## iter  30 value 54.761864
## iter  40 value 44.002860
## iter  50 value 38.964080
## iter  60 value 35.999156
## iter  70 value 34.889803
## iter  80 value 34.275483
## iter  90 value 33.092560
## iter 100 value 32.222294
## final  value 32.222294 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 612.391011 
## iter  10 value 107.606703
## iter  20 value 55.228656
## iter  30 value 33.454314
## iter  40 value 22.498684
## iter  50 value 18.604124
## iter  60 value 16.840289
## iter  70 value 14.475960
## iter  80 value 13.370640
## iter  90 value 11.006865
## iter 100 value 9.086214
## final  value 9.086214 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1072.892713 
## iter  10 value 129.538809
## iter  20 value 88.495833
## iter  30 value 66.684826
## iter  40 value 56.320553
## iter  50 value 48.891576
## iter  60 value 42.619101
## iter  70 value 38.637188
## iter  80 value 34.769146
## iter  90 value 33.492439
## iter 100 value 32.712303
## final  value 32.712303 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1081.565080 
## iter  10 value 173.187093
## iter  20 value 108.870143
## iter  30 value 77.582238
## iter  40 value 61.970684
## iter  50 value 53.940781
## iter  60 value 48.216406
## iter  70 value 46.544279
## iter  80 value 44.975749
## iter  90 value 43.201511
## iter 100 value 42.421860
## final  value 42.421860 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 743.643944 
## iter  10 value 123.620134
## iter  20 value 75.941257
## iter  30 value 49.410853
## iter  40 value 41.609866
## iter  50 value 35.672965
## iter  60 value 28.689463
## iter  70 value 26.939983
## iter  80 value 25.179475
## iter  90 value 24.811607
## iter 100 value 24.622894
## final  value 24.622894 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 988.789466 
## iter  10 value 99.983463
## iter  20 value 51.702645
## iter  30 value 33.824345
## iter  40 value 25.355096
## iter  50 value 22.841275
## iter  60 value 21.161674
## iter  70 value 19.936568
## iter  80 value 18.146773
## iter  90 value 16.563985
## iter 100 value 15.636602
## final  value 15.636602 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 714.491959 
## iter  10 value 150.636664
## iter  20 value 81.378114
## iter  30 value 65.007160
## iter  40 value 61.627349
## iter  50 value 56.211447
## iter  60 value 53.031447
## iter  70 value 51.141709
## iter  80 value 49.200515
## iter  90 value 42.615959
## iter 100 value 39.117740
## final  value 39.117740 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 920.517243 
## iter  10 value 96.995096
## iter  20 value 56.646709
## iter  30 value 36.184907
## iter  40 value 25.488053
## iter  50 value 20.737430
## iter  60 value 17.859962
## iter  70 value 16.753466
## iter  80 value 16.144359
## iter  90 value 15.581397
## iter 100 value 15.300634
## final  value 15.300634 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1192.896468 
## iter  10 value 141.731331
## iter  20 value 86.409191
## iter  30 value 62.770632
## iter  40 value 53.192199
## iter  50 value 46.227808
## iter  60 value 44.997631
## iter  70 value 44.328236
## iter  80 value 42.150693
## iter  90 value 41.483238
## iter 100 value 39.183053
## final  value 39.183053 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1034.489009 
## iter  10 value 94.141685
## iter  20 value 49.377146
## iter  30 value 37.291416
## iter  40 value 29.669647
## iter  50 value 27.376815
## iter  60 value 26.428703
## iter  70 value 26.150454
## iter  80 value 25.598177
## iter  90 value 24.960924
## iter 100 value 24.757509
## final  value 24.757509 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 727.452052 
## iter  10 value 104.069020
## iter  20 value 68.866751
## iter  30 value 52.614142
## iter  40 value 45.853369
## iter  50 value 39.102741
## iter  60 value 36.030594
## iter  70 value 35.093639
## iter  80 value 34.271340
## iter  90 value 32.055619
## iter 100 value 30.791856
## final  value 30.791856 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 886.515142 
## iter  10 value 104.120498
## iter  20 value 58.769857
## iter  30 value 40.088675
## iter  40 value 31.007802
## iter  50 value 26.097740
## iter  60 value 23.938573
## iter  70 value 21.892416
## iter  80 value 19.884201
## iter  90 value 17.209500
## iter 100 value 15.163245
## final  value 15.163245 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 670.938056 
## iter  10 value 114.454299
## iter  20 value 64.969495
## iter  30 value 40.452766
## iter  40 value 21.776160
## iter  50 value 15.801758
## iter  60 value 13.546994
## iter  70 value 12.440615
## iter  80 value 11.558910
## iter  90 value 9.766393
## iter 100 value 8.279670
## final  value 8.279670 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1072.802619 
## iter  10 value 96.603928
## iter  20 value 58.637133
## iter  30 value 37.681506
## iter  40 value 26.194665
## iter  50 value 22.804843
## iter  60 value 19.706216
## iter  70 value 17.269985
## iter  80 value 16.374900
## iter  90 value 15.739147
## iter 100 value 15.271339
## final  value 15.271339 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 773.378604 
## iter  10 value 109.632232
## iter  20 value 72.326257
## iter  30 value 42.408020
## iter  40 value 22.424320
## iter  50 value 17.314526
## iter  60 value 15.924501
## iter  70 value 14.846049
## iter  80 value 13.988408
## iter  90 value 13.393379
## iter 100 value 12.986404
## final  value 12.986404 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 751.720234 
## iter  10 value 110.043695
## iter  20 value 76.705716
## iter  30 value 29.731784
## iter  40 value 15.338498
## iter  50 value 11.916016
## iter  60 value 10.701987
## iter  70 value 9.637492
## iter  80 value 8.864219
## iter  90 value 8.306553
## iter 100 value 7.723524
## final  value 7.723524 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 954.106336 
## iter  10 value 150.857363
## iter  20 value 92.233952
## iter  30 value 54.765157
## iter  40 value 35.906675
## iter  50 value 24.915539
## iter  60 value 21.675732
## iter  70 value 19.767580
## iter  80 value 17.459531
## iter  90 value 16.406650
## iter 100 value 14.936460
## final  value 14.936460 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 760.010408 
## iter  10 value 106.438478
## iter  20 value 52.619258
## iter  30 value 31.025301
## iter  40 value 17.797312
## iter  50 value 13.043937
## iter  60 value 11.905158
## iter  70 value 8.651584
## iter  80 value 7.959431
## iter  90 value 7.116555
## iter 100 value 6.567975
## final  value 6.567975 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1037.129237 
## iter  10 value 113.021875
## iter  20 value 60.007383
## iter  30 value 34.767594
## iter  40 value 21.229064
## iter  50 value 13.108175
## iter  60 value 11.320932
## iter  70 value 10.615131
## iter  80 value 10.127448
## iter  90 value 9.616795
## iter 100 value 9.375701
## final  value 9.375701 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 783.373387 
## iter  10 value 117.701599
## iter  20 value 64.549780
## iter  30 value 34.890939
## iter  40 value 21.737646
## iter  50 value 19.186548
## iter  60 value 16.845404
## iter  70 value 14.835302
## iter  80 value 13.596191
## iter  90 value 11.614323
## iter 100 value 9.992668
## final  value 9.992668 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1333.706881 
## iter  10 value 138.722775
## iter  20 value 92.484012
## iter  30 value 68.145282
## iter  40 value 45.989597
## iter  50 value 29.769829
## iter  60 value 20.020019
## iter  70 value 14.922119
## iter  80 value 13.324450
## iter  90 value 11.985143
## iter 100 value 11.179102
## final  value 11.179102 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1011.953538 
## iter  10 value 128.713270
## iter  20 value 87.650425
## iter  30 value 56.753691
## iter  40 value 38.595661
## iter  50 value 30.899842
## iter  60 value 28.702857
## iter  70 value 26.471884
## iter  80 value 24.680239
## iter  90 value 23.046147
## iter 100 value 22.199624
## final  value 22.199624 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1084.072108 
## iter  10 value 176.687288
## iter  20 value 99.941228
## iter  30 value 73.917753
## iter  40 value 48.754845
## iter  50 value 34.753308
## iter  60 value 23.518233
## iter  70 value 16.978114
## iter  80 value 14.121290
## iter  90 value 13.055412
## iter 100 value 12.078578
## final  value 12.078578 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 754.582674 
## iter  10 value 109.176795
## iter  20 value 71.474666
## iter  30 value 36.022652
## iter  40 value 23.530020
## iter  50 value 17.631559
## iter  60 value 16.481181
## iter  70 value 15.045626
## iter  80 value 13.399574
## iter  90 value 12.679408
## iter 100 value 11.981917
## final  value 11.981917 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 902.265141 
## iter  10 value 117.104768
## iter  20 value 76.156891
## iter  30 value 54.286808
## iter  40 value 42.014514
## iter  50 value 38.576146
## iter  60 value 33.757208
## iter  70 value 28.948178
## iter  80 value 26.440943
## iter  90 value 22.405969
## iter 100 value 19.270105
## final  value 19.270105 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1054.855003 
## iter  10 value 125.766035
## iter  20 value 82.885786
## iter  30 value 61.103378
## iter  40 value 44.208154
## iter  50 value 35.634222
## iter  60 value 30.189542
## iter  70 value 25.295118
## iter  80 value 23.619924
## iter  90 value 22.518636
## iter 100 value 20.479690
## final  value 20.479690 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 902.717893 
## iter  10 value 136.309277
## iter  20 value 90.240364
## iter  30 value 58.081834
## iter  40 value 43.895147
## iter  50 value 34.428651
## iter  60 value 28.066273
## iter  70 value 23.690617
## iter  80 value 21.254725
## iter  90 value 19.720396
## iter 100 value 18.698074
## final  value 18.698074 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 916.690192 
## iter  10 value 121.698104
## iter  20 value 72.619332
## iter  30 value 58.799122
## iter  40 value 50.276537
## iter  50 value 45.751423
## iter  60 value 40.823529
## iter  70 value 37.602293
## iter  80 value 34.876944
## iter  90 value 33.131314
## iter 100 value 31.660954
## final  value 31.660954 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 964.895633 
## iter  10 value 106.611654
## iter  20 value 85.244458
## iter  30 value 60.905401
## iter  40 value 40.058865
## iter  50 value 34.283580
## iter  60 value 33.062116
## iter  70 value 32.169929
## iter  80 value 31.447254
## iter  90 value 31.141419
## iter 100 value 30.738030
## final  value 30.738030 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 752.381702 
## iter  10 value 99.244651
## iter  20 value 68.748262
## iter  30 value 47.012342
## iter  40 value 34.230097
## iter  50 value 28.026791
## iter  60 value 25.816259
## iter  70 value 22.014581
## iter  80 value 18.266715
## iter  90 value 16.888009
## iter 100 value 15.844176
## final  value 15.844176 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1258.191345 
## iter  10 value 150.511712
## iter  20 value 94.331093
## iter  30 value 71.748477
## iter  40 value 62.408509
## iter  50 value 53.014862
## iter  60 value 47.834497
## iter  70 value 44.857315
## iter  80 value 42.829384
## iter  90 value 39.308058
## iter 100 value 37.228979
## final  value 37.228979 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 898.641686 
## iter  10 value 102.487546
## iter  20 value 71.736480
## iter  30 value 37.044427
## iter  40 value 22.659463
## iter  50 value 17.140177
## iter  60 value 15.936979
## iter  70 value 15.496508
## iter  80 value 15.101374
## iter  90 value 14.848090
## iter 100 value 13.780362
## final  value 13.780362 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 952.085910 
## iter  10 value 164.096544
## iter  20 value 124.016153
## iter  30 value 97.797020
## iter  40 value 79.470594
## iter  50 value 69.268924
## iter  60 value 61.006596
## iter  70 value 53.643199
## iter  80 value 47.406315
## iter  90 value 44.094019
## iter 100 value 42.962169
## final  value 42.962169 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1302.389206 
## iter  10 value 127.764523
## iter  20 value 64.320551
## iter  30 value 33.145037
## iter  40 value 20.671370
## iter  50 value 15.673082
## iter  60 value 12.943873
## iter  70 value 10.275648
## iter  80 value 7.277816
## iter  90 value 4.930378
## iter 100 value 4.036511
## final  value 4.036511 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 583.055291 
## iter  10 value 154.585478
## iter  20 value 78.966740
## iter  30 value 54.083050
## iter  40 value 43.667301
## iter  50 value 38.156206
## iter  60 value 35.118465
## iter  70 value 33.652586
## iter  80 value 32.452646
## iter  90 value 31.006417
## iter 100 value 30.453666
## final  value 30.453666 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 663.474614 
## iter  10 value 142.697585
## iter  20 value 85.154749
## iter  30 value 53.477575
## iter  40 value 38.428292
## iter  50 value 28.775161
## iter  60 value 25.484140
## iter  70 value 22.825050
## iter  80 value 19.902972
## iter  90 value 18.732300
## iter 100 value 17.745207
## final  value 17.745207 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1023.047758 
## iter  10 value 145.963766
## iter  20 value 60.132909
## iter  30 value 50.263386
## iter  40 value 46.989503
## iter  50 value 43.183065
## iter  60 value 39.460544
## iter  70 value 36.596356
## iter  80 value 34.679832
## iter  90 value 33.227983
## iter 100 value 30.267226
## final  value 30.267226 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 952.046084 
## iter  10 value 125.063727
## iter  20 value 71.964821
## iter  30 value 48.558642
## iter  40 value 41.013971
## iter  50 value 34.727686
## iter  60 value 31.388954
## iter  70 value 30.446693
## iter  80 value 29.611571
## iter  90 value 28.025810
## iter 100 value 27.042413
## final  value 27.042413 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1183.436509 
## iter  10 value 131.741609
## iter  20 value 82.692421
## iter  30 value 53.602395
## iter  40 value 30.711527
## iter  50 value 23.132727
## iter  60 value 15.728025
## iter  70 value 13.765730
## iter  80 value 10.938157
## iter  90 value 9.789564
## iter 100 value 8.894300
## final  value 8.894300 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1016.891085 
## iter  10 value 119.448016
## iter  20 value 78.475565
## iter  30 value 41.732168
## iter  40 value 23.296871
## iter  50 value 18.534543
## iter  60 value 17.250475
## iter  70 value 16.256496
## iter  80 value 14.650397
## iter  90 value 13.404327
## iter 100 value 12.816380
## final  value 12.816380 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 646.126152 
## iter  10 value 86.942899
## iter  20 value 53.207492
## iter  30 value 29.167207
## iter  40 value 22.025149
## iter  50 value 20.870664
## iter  60 value 20.368303
## iter  70 value 20.003486
## iter  80 value 19.699779
## iter  90 value 19.422933
## iter 100 value 18.997549
## final  value 18.997549 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1582.283995 
## iter  10 value 113.548862
## iter  20 value 72.790220
## iter  30 value 46.150549
## iter  40 value 31.462076
## iter  50 value 20.917588
## iter  60 value 17.905803
## iter  70 value 15.951484
## iter  80 value 14.817623
## iter  90 value 13.910068
## iter 100 value 13.093907
## final  value 13.093907 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 727.049336 
## iter  10 value 121.279500
## iter  20 value 80.517824
## iter  30 value 52.243709
## iter  40 value 32.079452
## iter  50 value 23.943246
## iter  60 value 22.615879
## iter  70 value 21.419910
## iter  80 value 20.750071
## iter  90 value 18.915077
## iter 100 value 18.183069
## final  value 18.183069 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1300.940199 
## iter  10 value 131.286156
## iter  20 value 82.259029
## iter  30 value 56.940543
## iter  40 value 36.481342
## iter  50 value 25.903445
## iter  60 value 16.526503
## iter  70 value 13.775819
## iter  80 value 10.436062
## iter  90 value 9.014255
## iter 100 value 8.399541
## final  value 8.399541 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1240.149234 
## iter  10 value 173.570682
## iter  20 value 109.441434
## iter  30 value 90.531218
## iter  40 value 66.181428
## iter  50 value 54.599363
## iter  60 value 48.084035
## iter  70 value 44.039228
## iter  80 value 41.814519
## iter  90 value 39.904137
## iter 100 value 38.487606
## final  value 38.487606 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1246.201196 
## iter  10 value 121.542138
## iter  20 value 82.181344
## iter  30 value 68.017670
## iter  40 value 54.690247
## iter  50 value 47.203375
## iter  60 value 45.234047
## iter  70 value 43.964907
## iter  80 value 41.752586
## iter  90 value 39.883296
## iter 100 value 38.192099
## final  value 38.192099 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1441.547904 
## iter  10 value 142.528036
## iter  20 value 98.228962
## iter  30 value 64.184387
## iter  40 value 42.556055
## iter  50 value 20.761241
## iter  60 value 12.189263
## iter  70 value 9.184198
## iter  80 value 7.453496
## iter  90 value 6.494017
## iter 100 value 6.138853
## final  value 6.138853 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1187.932612 
## iter  10 value 116.201117
## iter  20 value 64.916926
## iter  30 value 42.003614
## iter  40 value 22.620622
## iter  50 value 19.786402
## iter  60 value 18.725443
## iter  70 value 18.179109
## iter  80 value 17.833247
## iter  90 value 14.515587
## iter 100 value 12.886338
## final  value 12.886338 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1723.504190 
## iter  10 value 104.522189
## iter  20 value 66.306371
## iter  30 value 38.224853
## iter  40 value 30.238482
## iter  50 value 27.173298
## iter  60 value 25.403374
## iter  70 value 22.950287
## iter  80 value 22.541265
## iter  90 value 22.222791
## iter 100 value 21.534085
## final  value 21.534085 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1036.601799 
## iter  10 value 92.963684
## iter  20 value 60.632959
## iter  30 value 32.799611
## iter  40 value 18.863264
## iter  50 value 15.136852
## iter  60 value 13.843870
## iter  70 value 13.313945
## iter  80 value 13.028775
## iter  90 value 12.817327
## iter 100 value 12.728948
## final  value 12.728948 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1308.225075 
## iter  10 value 171.921228
## iter  20 value 107.709174
## iter  30 value 83.588807
## iter  40 value 69.595335
## iter  50 value 53.303152
## iter  60 value 45.059638
## iter  70 value 37.187075
## iter  80 value 32.302497
## iter  90 value 29.355884
## iter 100 value 28.144072
## final  value 28.144072 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1177.581773 
## iter  10 value 122.968195
## iter  20 value 67.790671
## iter  30 value 49.267191
## iter  40 value 41.330666
## iter  50 value 36.990779
## iter  60 value 34.595856
## iter  70 value 33.330627
## iter  80 value 31.739790
## iter  90 value 29.656407
## iter 100 value 28.380152
## final  value 28.380152 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 658.243303 
## iter  10 value 95.493852
## iter  20 value 67.574690
## iter  30 value 44.979731
## iter  40 value 35.913839
## iter  50 value 32.922822
## iter  60 value 30.097204
## iter  70 value 28.728245
## iter  80 value 27.712424
## iter  90 value 26.664873
## iter 100 value 26.280708
## final  value 26.280708 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1052.177279 
## iter  10 value 108.937687
## iter  20 value 68.046494
## iter  30 value 45.000628
## iter  40 value 36.587075
## iter  50 value 32.366837
## iter  60 value 29.280544
## iter  70 value 27.109424
## iter  80 value 26.039078
## iter  90 value 25.085060
## iter 100 value 22.131925
## final  value 22.131925 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 886.967737 
## iter  10 value 108.086771
## iter  20 value 73.787438
## iter  30 value 56.343122
## iter  40 value 48.486131
## iter  50 value 43.067322
## iter  60 value 40.915868
## iter  70 value 36.774763
## iter  80 value 33.224767
## iter  90 value 30.090174
## iter 100 value 27.953948
## final  value 27.953948 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 678.450142 
## iter  10 value 98.501976
## iter  20 value 58.470880
## iter  30 value 32.329661
## iter  40 value 16.544653
## iter  50 value 12.992758
## iter  60 value 11.476906
## iter  70 value 9.667768
## iter  80 value 8.697852
## iter  90 value 8.201432
## iter 100 value 8.009683
## final  value 8.009683 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1322.037794 
## iter  10 value 159.553359
## iter  20 value 113.903287
## iter  30 value 80.939239
## iter  40 value 56.289757
## iter  50 value 46.942378
## iter  60 value 43.220430
## iter  70 value 40.576494
## iter  80 value 37.953481
## iter  90 value 37.221041
## iter 100 value 36.174173
## final  value 36.174173 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 880.155879 
## iter  10 value 147.913959
## iter  20 value 112.095809
## iter  30 value 93.046482
## iter  40 value 79.489243
## iter  50 value 75.082918
## iter  60 value 69.841631
## iter  70 value 65.131151
## iter  80 value 63.520451
## iter  90 value 62.013485
## iter 100 value 61.112725
## final  value 61.112725 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 657.971505 
## iter  10 value 129.243874
## iter  20 value 89.834363
## iter  30 value 64.163624
## iter  40 value 32.216601
## iter  50 value 20.266372
## iter  60 value 17.061823
## iter  70 value 15.871024
## iter  80 value 15.161973
## iter  90 value 13.711989
## iter 100 value 12.560960
## final  value 12.560960 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 966.706656 
## iter  10 value 134.763633
## iter  20 value 96.386360
## iter  30 value 71.077871
## iter  40 value 59.516224
## iter  50 value 54.074357
## iter  60 value 51.198647
## iter  70 value 48.958223
## iter  80 value 47.186068
## iter  90 value 46.000071
## iter 100 value 43.283079
## final  value 43.283079 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1074.924645 
## iter  10 value 119.831360
## iter  20 value 79.899708
## iter  30 value 49.934371
## iter  40 value 35.955252
## iter  50 value 28.625384
## iter  60 value 22.355681
## iter  70 value 17.939550
## iter  80 value 16.646650
## iter  90 value 16.099093
## iter 100 value 15.759590
## final  value 15.759590 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1301.393646 
## iter  10 value 102.162824
## iter  20 value 61.164923
## iter  30 value 36.579365
## iter  40 value 23.291447
## iter  50 value 20.025014
## iter  60 value 19.205744
## iter  70 value 18.718706
## iter  80 value 18.375260
## iter  90 value 18.060334
## iter 100 value 17.625453
## final  value 17.625453 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1116.013984 
## iter  10 value 173.485159
## iter  20 value 107.133271
## iter  30 value 87.773562
## iter  40 value 76.505615
## iter  50 value 74.150877
## iter  60 value 71.964304
## iter  70 value 69.597838
## iter  80 value 68.628793
## iter  90 value 67.135874
## iter 100 value 65.309375
## final  value 65.309375 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 622.654992 
## iter  10 value 103.512679
## iter  20 value 67.935740
## iter  30 value 40.658400
## iter  40 value 30.569564
## iter  50 value 27.533643
## iter  60 value 26.367998
## iter  70 value 25.176183
## iter  80 value 24.089319
## iter  90 value 23.117741
## iter 100 value 22.788131
## final  value 22.788131 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 958.708764 
## iter  10 value 126.626833
## iter  20 value 61.772677
## iter  30 value 48.037003
## iter  40 value 42.893765
## iter  50 value 41.558365
## iter  60 value 39.657384
## iter  70 value 39.012518
## iter  80 value 38.589229
## iter  90 value 38.233166
## iter 100 value 37.126494
## final  value 37.126494 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1447.118179 
## iter  10 value 105.404476
## iter  20 value 70.565630
## iter  30 value 43.823166
## iter  40 value 35.079124
## iter  50 value 29.072285
## iter  60 value 27.838233
## iter  70 value 26.953312
## iter  80 value 26.088201
## iter  90 value 25.297110
## iter 100 value 22.837699
## final  value 22.837699 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 986.906288 
## iter  10 value 114.909388
## iter  20 value 76.340104
## iter  30 value 53.473613
## iter  40 value 31.959055
## iter  50 value 15.331188
## iter  60 value 8.766322
## iter  70 value 6.453551
## iter  80 value 5.624861
## iter  90 value 4.943016
## iter 100 value 4.492577
## final  value 4.492577 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 762.837202 
## iter  10 value 131.440839
## iter  20 value 78.347263
## iter  30 value 54.408775
## iter  40 value 38.921015
## iter  50 value 35.610960
## iter  60 value 34.078641
## iter  70 value 31.768408
## iter  80 value 30.380991
## iter  90 value 29.488631
## iter 100 value 28.096294
## final  value 28.096294 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1670.103243 
## iter  10 value 134.057537
## iter  20 value 97.986434
## iter  30 value 75.746558
## iter  40 value 62.774028
## iter  50 value 52.381660
## iter  60 value 47.362211
## iter  70 value 42.887415
## iter  80 value 41.487605
## iter  90 value 38.618833
## iter 100 value 35.487568
## final  value 35.487568 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1067.057239 
## iter  10 value 151.376577
## iter  20 value 89.643678
## iter  30 value 64.338365
## iter  40 value 49.684368
## iter  50 value 43.973080
## iter  60 value 41.415160
## iter  70 value 39.921308
## iter  80 value 38.830269
## iter  90 value 37.802911
## iter 100 value 37.351517
## final  value 37.351517 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 876.035778 
## iter  10 value 113.211449
## iter  20 value 65.940480
## iter  30 value 37.450190
## iter  40 value 22.254023
## iter  50 value 19.776181
## iter  60 value 18.265624
## iter  70 value 17.268010
## iter  80 value 16.707618
## iter  90 value 16.286017
## iter 100 value 15.773495
## final  value 15.773495 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 973.320352 
## iter  10 value 128.956373
## iter  20 value 97.250805
## iter  30 value 77.155919
## iter  40 value 64.978701
## iter  50 value 59.409812
## iter  60 value 55.838887
## iter  70 value 53.188994
## iter  80 value 49.899683
## iter  90 value 47.761841
## iter 100 value 46.055060
## final  value 46.055060 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 581.933434 
## iter  10 value 108.071319
## iter  20 value 74.292035
## iter  30 value 57.404394
## iter  40 value 39.848862
## iter  50 value 32.497520
## iter  60 value 30.402098
## iter  70 value 28.776759
## iter  80 value 26.952412
## iter  90 value 25.259809
## iter 100 value 23.649393
## final  value 23.649393 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 736.393933 
## iter  10 value 101.315317
## iter  20 value 59.027277
## iter  30 value 36.236675
## iter  40 value 24.023246
## iter  50 value 21.914687
## iter  60 value 21.061347
## iter  70 value 19.832100
## iter  80 value 18.781146
## iter  90 value 18.227434
## iter 100 value 17.607662
## final  value 17.607662 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1507.194092 
## iter  10 value 156.754529
## iter  20 value 100.353689
## iter  30 value 82.813168
## iter  40 value 61.572796
## iter  50 value 48.900051
## iter  60 value 43.405475
## iter  70 value 39.910654
## iter  80 value 38.218925
## iter  90 value 37.352326
## iter 100 value 36.721534
## final  value 36.721534 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 749.160031 
## iter  10 value 103.303548
## iter  20 value 50.269006
## iter  30 value 19.960822
## iter  40 value 12.559027
## iter  50 value 10.394599
## iter  60 value 8.023343
## iter  70 value 4.261491
## iter  80 value 3.380974
## iter  90 value 2.978823
## iter 100 value 2.641745
## final  value 2.641745 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1007.183742 
## iter  10 value 94.882244
## iter  20 value 57.616501
## iter  30 value 37.861314
## iter  40 value 18.234199
## iter  50 value 13.122146
## iter  60 value 11.359597
## iter  70 value 9.490115
## iter  80 value 8.433435
## iter  90 value 7.864330
## iter 100 value 7.431229
## final  value 7.431229 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 838.448811 
## iter  10 value 113.236945
## iter  20 value 64.267963
## iter  30 value 36.953424
## iter  40 value 22.430448
## iter  50 value 15.842709
## iter  60 value 12.109406
## iter  70 value 9.376086
## iter  80 value 8.821272
## iter  90 value 8.472459
## iter 100 value 8.307185
## final  value 8.307185 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 919.911434 
## iter  10 value 119.401038
## iter  20 value 74.124010
## iter  30 value 50.428999
## iter  40 value 35.513102
## iter  50 value 30.411000
## iter  60 value 22.476228
## iter  70 value 17.925414
## iter  80 value 15.893892
## iter  90 value 14.647302
## iter 100 value 13.773476
## final  value 13.773476 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1074.140422 
## iter  10 value 150.832903
## iter  20 value 87.718032
## iter  30 value 63.323145
## iter  40 value 54.479646
## iter  50 value 52.416131
## iter  60 value 51.048677
## iter  70 value 46.356073
## iter  80 value 45.630798
## iter  90 value 41.904856
## iter 100 value 39.971485
## final  value 39.971485 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1264.398826 
## iter  10 value 132.528423
## iter  20 value 77.080745
## iter  30 value 54.960494
## iter  40 value 45.989496
## iter  50 value 43.495586
## iter  60 value 42.853533
## iter  70 value 42.090077
## iter  80 value 41.477755
## iter  90 value 40.563421
## iter 100 value 39.522679
## final  value 39.522679 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1233.877787 
## iter  10 value 157.268376
## iter  20 value 93.812835
## iter  30 value 57.635528
## iter  40 value 38.329201
## iter  50 value 34.452157
## iter  60 value 31.159711
## iter  70 value 29.442957
## iter  80 value 28.036074
## iter  90 value 26.584490
## iter 100 value 24.472030
## final  value 24.472030 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 803.901867 
## iter  10 value 127.128175
## iter  20 value 79.458983
## iter  30 value 60.407971
## iter  40 value 43.541535
## iter  50 value 34.394286
## iter  60 value 30.383093
## iter  70 value 27.940053
## iter  80 value 24.517397
## iter  90 value 21.260865
## iter 100 value 20.686779
## final  value 20.686779 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 637.093382 
## iter  10 value 135.213543
## iter  20 value 55.114466
## iter  30 value 35.214824
## iter  40 value 29.979029
## iter  50 value 27.887886
## iter  60 value 26.187198
## iter  70 value 25.414628
## iter  80 value 24.890089
## iter  90 value 24.134694
## iter 100 value 21.906774
## final  value 21.906774 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1124.179104 
## iter  10 value 126.486587
## iter  20 value 66.062206
## iter  30 value 47.076737
## iter  40 value 34.540671
## iter  50 value 28.648815
## iter  60 value 26.835443
## iter  70 value 24.971805
## iter  80 value 23.603740
## iter  90 value 22.446222
## iter 100 value 21.309889
## final  value 21.309889 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1048.785943 
## iter  10 value 118.019049
## iter  20 value 73.229787
## iter  30 value 55.045021
## iter  40 value 44.192043
## iter  50 value 40.843984
## iter  60 value 39.369914
## iter  70 value 38.814363
## iter  80 value 38.411310
## iter  90 value 37.998364
## iter 100 value 37.854640
## final  value 37.854640 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1117.551259 
## iter  10 value 160.856041
## iter  20 value 108.342793
## iter  30 value 90.697886
## iter  40 value 82.243140
## iter  50 value 78.532443
## iter  60 value 76.323415
## iter  70 value 71.955475
## iter  80 value 68.834383
## iter  90 value 66.344544
## iter 100 value 64.825128
## final  value 64.825128 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1006.347089 
## iter  10 value 98.427079
## iter  20 value 65.461709
## iter  30 value 41.984287
## iter  40 value 32.620214
## iter  50 value 29.822886
## iter  60 value 28.731104
## iter  70 value 28.252177
## iter  80 value 27.951383
## iter  90 value 26.596132
## iter 100 value 24.788683
## final  value 24.788683 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 712.888680 
## iter  10 value 122.359195
## iter  20 value 82.473362
## iter  30 value 60.686189
## iter  40 value 51.371003
## iter  50 value 42.495191
## iter  60 value 37.187990
## iter  70 value 35.524217
## iter  80 value 34.113698
## iter  90 value 33.385242
## iter 100 value 32.770662
## final  value 32.770662 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 973.709615 
## iter  10 value 123.123248
## iter  20 value 68.629584
## iter  30 value 53.592225
## iter  40 value 41.989607
## iter  50 value 34.780545
## iter  60 value 28.454467
## iter  70 value 26.078283
## iter  80 value 25.203825
## iter  90 value 24.495854
## iter 100 value 24.139989
## final  value 24.139989 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 730.885570 
## iter  10 value 144.165372
## iter  20 value 53.777133
## iter  30 value 28.144509
## iter  40 value 16.428575
## iter  50 value 13.676046
## iter  60 value 12.587461
## iter  70 value 10.636616
## iter  80 value 8.731839
## iter  90 value 8.225439
## iter 100 value 6.457375
## final  value 6.457375 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 705.208391 
## iter  10 value 99.484632
## iter  20 value 78.643007
## iter  30 value 58.378434
## iter  40 value 43.368189
## iter  50 value 37.328547
## iter  60 value 30.210300
## iter  70 value 25.879215
## iter  80 value 24.160951
## iter  90 value 20.442843
## iter 100 value 19.134106
## final  value 19.134106 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1229.197137 
## iter  10 value 161.745222
## iter  20 value 116.686440
## iter  30 value 98.429882
## iter  40 value 87.445776
## iter  50 value 75.336979
## iter  60 value 62.759346
## iter  70 value 57.350810
## iter  80 value 51.898649
## iter  90 value 48.927635
## iter 100 value 46.814485
## final  value 46.814485 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 686.323778 
## iter  10 value 117.746585
## iter  20 value 83.223841
## iter  30 value 50.567364
## iter  40 value 36.381746
## iter  50 value 29.678476
## iter  60 value 26.840983
## iter  70 value 25.855474
## iter  80 value 24.767319
## iter  90 value 23.777623
## iter 100 value 23.325025
## final  value 23.325025 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 873.077666 
## iter  10 value 251.904247
## iter  20 value 205.821684
## iter  30 value 130.359894
## iter  40 value 88.689489
## iter  50 value 71.889794
## iter  60 value 67.616850
## iter  70 value 63.709439
## iter  80 value 61.523605
## iter  90 value 57.329331
## iter 100 value 52.773484
## final  value 52.773484 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 629.574647 
## iter  10 value 131.509578
## iter  20 value 84.265800
## iter  30 value 53.935444
## iter  40 value 41.622321
## iter  50 value 34.099153
## iter  60 value 28.411630
## iter  70 value 25.112422
## iter  80 value 23.883445
## iter  90 value 22.905615
## iter 100 value 22.080140
## final  value 22.080140 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 849.909391 
## iter  10 value 123.825976
## iter  20 value 93.739242
## iter  30 value 71.848938
## iter  40 value 56.871110
## iter  50 value 50.477434
## iter  60 value 46.126737
## iter  70 value 43.527939
## iter  80 value 39.365048
## iter  90 value 38.217009
## iter 100 value 36.541493
## final  value 36.541493 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 665.280011 
## iter  10 value 138.061393
## iter  20 value 98.322618
## iter  30 value 78.873853
## iter  40 value 59.587999
## iter  50 value 50.184220
## iter  60 value 45.304378
## iter  70 value 40.530694
## iter  80 value 36.792860
## iter  90 value 32.925392
## iter 100 value 31.466505
## final  value 31.466505 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1026.463376 
## iter  10 value 157.183380
## iter  20 value 116.773153
## iter  30 value 82.056269
## iter  40 value 65.256075
## iter  50 value 45.361886
## iter  60 value 37.103921
## iter  70 value 35.259690
## iter  80 value 33.455919
## iter  90 value 31.827247
## iter 100 value 30.794873
## final  value 30.794873 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 613.463518 
## iter  10 value 106.058588
## iter  20 value 61.003764
## iter  30 value 35.932843
## iter  40 value 21.853358
## iter  50 value 15.760834
## iter  60 value 14.544925
## iter  70 value 13.467181
## iter  80 value 12.690188
## iter  90 value 11.798217
## iter 100 value 11.067703
## final  value 11.067703 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1093.137837 
## iter  10 value 83.852904
## iter  20 value 35.072338
## iter  30 value 16.639335
## iter  40 value 12.923793
## iter  50 value 11.747341
## iter  60 value 11.301598
## iter  70 value 10.191396
## iter  80 value 8.557863
## iter  90 value 6.907521
## iter 100 value 5.792462
## final  value 5.792462 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1186.883896 
## iter  10 value 88.001749
## iter  20 value 51.452118
## iter  30 value 27.814892
## iter  40 value 19.065180
## iter  50 value 15.629486
## iter  60 value 14.921401
## iter  70 value 14.617823
## iter  80 value 14.391183
## iter  90 value 12.741627
## iter 100 value 12.079037
## final  value 12.079037 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 776.431878 
## iter  10 value 98.189762
## iter  20 value 63.414726
## iter  30 value 38.102220
## iter  40 value 27.849111
## iter  50 value 21.431707
## iter  60 value 17.790970
## iter  70 value 16.511732
## iter  80 value 15.708078
## iter  90 value 14.537453
## iter 100 value 14.101078
## final  value 14.101078 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 884.593771 
## iter  10 value 75.758289
## iter  20 value 45.231695
## iter  30 value 25.894201
## iter  40 value 13.305748
## iter  50 value 10.536901
## iter  60 value 9.575706
## iter  70 value 8.730783
## iter  80 value 8.203675
## iter  90 value 7.886139
## iter 100 value 7.673461
## final  value 7.673461 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1221.493786 
## iter  10 value 138.424485
## iter  20 value 84.160631
## iter  30 value 53.888254
## iter  40 value 33.888575
## iter  50 value 25.777309
## iter  60 value 23.412021
## iter  70 value 22.173367
## iter  80 value 20.897775
## iter  90 value 19.927815
## iter 100 value 15.775553
## final  value 15.775553 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 649.352485 
## iter  10 value 92.766063
## iter  20 value 60.106588
## iter  30 value 35.545316
## iter  40 value 29.768942
## iter  50 value 26.662276
## iter  60 value 24.603940
## iter  70 value 22.426071
## iter  80 value 21.836614
## iter  90 value 21.590443
## iter 100 value 21.412941
## final  value 21.412941 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1300.371430 
## iter  10 value 106.754346
## iter  20 value 60.832573
## iter  30 value 41.366622
## iter  40 value 30.071765
## iter  50 value 24.595219
## iter  60 value 16.053617
## iter  70 value 11.038256
## iter  80 value 10.269525
## iter  90 value 9.870124
## iter 100 value 9.489435
## final  value 9.489435 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 622.415982 
## iter  10 value 83.867198
## iter  20 value 49.294275
## iter  30 value 36.892249
## iter  40 value 25.852047
## iter  50 value 18.786648
## iter  60 value 17.324171
## iter  70 value 16.838201
## iter  80 value 16.389900
## iter  90 value 16.026828
## iter 100 value 15.640544
## final  value 15.640544 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1388.276788 
## iter  10 value 90.227970
## iter  20 value 59.307349
## iter  30 value 31.551268
## iter  40 value 14.819713
## iter  50 value 8.400953
## iter  60 value 5.810941
## iter  70 value 5.141554
## iter  80 value 4.642290
## iter  90 value 4.222428
## iter 100 value 4.039826
## final  value 4.039826 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1341.812895 
## iter  10 value 102.620850
## iter  20 value 53.117274
## iter  30 value 33.477437
## iter  40 value 26.807496
## iter  50 value 23.834630
## iter  60 value 20.471421
## iter  70 value 16.468304
## iter  80 value 15.490810
## iter  90 value 14.745578
## iter 100 value 12.327694
## final  value 12.327694 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1428.061525 
## iter  10 value 98.080502
## iter  20 value 62.034888
## iter  30 value 42.175145
## iter  40 value 32.008377
## iter  50 value 26.322553
## iter  60 value 24.659389
## iter  70 value 22.581619
## iter  80 value 22.131209
## iter  90 value 21.639036
## iter 100 value 19.887164
## final  value 19.887164 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 697.453652 
## iter  10 value 129.317034
## iter  20 value 85.804158
## iter  30 value 57.523674
## iter  40 value 48.070305
## iter  50 value 43.141697
## iter  60 value 40.406987
## iter  70 value 38.095786
## iter  80 value 35.223600
## iter  90 value 32.914374
## iter 100 value 31.102705
## final  value 31.102705 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1047.400779 
## iter  10 value 110.626107
## iter  20 value 61.660980
## iter  30 value 36.885126
## iter  40 value 16.408272
## iter  50 value 10.214304
## iter  60 value 8.394365
## iter  70 value 7.659440
## iter  80 value 6.907002
## iter  90 value 6.568888
## iter 100 value 6.308883
## final  value 6.308883 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 882.143778 
## iter  10 value 129.392034
## iter  20 value 92.788120
## iter  30 value 65.588992
## iter  40 value 50.167884
## iter  50 value 45.669178
## iter  60 value 44.276873
## iter  70 value 42.619741
## iter  80 value 41.158201
## iter  90 value 39.348750
## iter 100 value 34.523313
## final  value 34.523313 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1792.120670 
## iter  10 value 128.842807
## iter  20 value 74.051793
## iter  30 value 53.692603
## iter  40 value 37.728050
## iter  50 value 29.161711
## iter  60 value 24.123147
## iter  70 value 22.151510
## iter  80 value 20.158639
## iter  90 value 18.800232
## iter 100 value 17.667960
## final  value 17.667960 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1054.502002 
## iter  10 value 132.290711
## iter  20 value 61.776124
## iter  30 value 34.716406
## iter  40 value 18.917255
## iter  50 value 11.520558
## iter  60 value 8.056793
## iter  70 value 6.835824
## iter  80 value 6.396021
## iter  90 value 6.111974
## iter 100 value 5.737433
## final  value 5.737433 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 881.345597 
## iter  10 value 126.544856
## iter  20 value 73.616827
## iter  30 value 45.466230
## iter  40 value 35.595798
## iter  50 value 29.831187
## iter  60 value 28.076862
## iter  70 value 27.061126
## iter  80 value 26.217112
## iter  90 value 25.264435
## iter 100 value 24.581129
## final  value 24.581129 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 749.306101 
## iter  10 value 107.134771
## iter  20 value 67.709630
## iter  30 value 45.326114
## iter  40 value 36.102237
## iter  50 value 33.121811
## iter  60 value 29.308682
## iter  70 value 22.651062
## iter  80 value 20.092318
## iter  90 value 18.944342
## iter 100 value 17.826286
## final  value 17.826286 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1158.575581 
## iter  10 value 172.657914
## iter  20 value 102.337970
## iter  30 value 80.368028
## iter  40 value 61.059048
## iter  50 value 48.140863
## iter  60 value 37.942430
## iter  70 value 32.598648
## iter  80 value 29.350860
## iter  90 value 26.766053
## iter 100 value 25.609738
## final  value 25.609738 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1632.704693 
## iter  10 value 123.980119
## iter  20 value 60.002867
## iter  30 value 45.400155
## iter  40 value 32.025905
## iter  50 value 25.004500
## iter  60 value 21.423647
## iter  70 value 19.963737
## iter  80 value 18.467920
## iter  90 value 16.396914
## iter 100 value 15.718884
## final  value 15.718884 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1028.394974 
## iter  10 value 151.268022
## iter  20 value 100.105705
## iter  30 value 74.458310
## iter  40 value 60.312498
## iter  50 value 56.871453
## iter  60 value 51.475894
## iter  70 value 44.206816
## iter  80 value 38.122663
## iter  90 value 36.460322
## iter 100 value 34.374271
## final  value 34.374271 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1099.421992 
## iter  10 value 172.002809
## iter  20 value 106.989256
## iter  30 value 91.141799
## iter  40 value 77.554755
## iter  50 value 70.667446
## iter  60 value 67.895934
## iter  70 value 65.914131
## iter  80 value 60.409017
## iter  90 value 59.612858
## iter 100 value 59.052468
## final  value 59.052468 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1659.565369 
## iter  10 value 152.122793
## iter  20 value 104.241292
## iter  30 value 85.671362
## iter  40 value 71.274518
## iter  50 value 62.878175
## iter  60 value 56.316856
## iter  70 value 53.386421
## iter  80 value 51.815737
## iter  90 value 51.005989
## iter 100 value 50.354222
## final  value 50.354222 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1117.873977 
## iter  10 value 126.735013
## iter  20 value 91.708796
## iter  30 value 64.921087
## iter  40 value 51.564586
## iter  50 value 39.129304
## iter  60 value 33.390231
## iter  70 value 29.299760
## iter  80 value 25.001638
## iter  90 value 23.059061
## iter 100 value 22.067252
## final  value 22.067252 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1045.750357 
## iter  10 value 216.451799
## iter  20 value 97.620590
## iter  30 value 47.684501
## iter  40 value 29.093954
## iter  50 value 18.294517
## iter  60 value 14.374176
## iter  70 value 13.573437
## iter  80 value 13.011800
## iter  90 value 12.403657
## iter 100 value 11.706221
## final  value 11.706221 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 784.325651 
## iter  10 value 120.686497
## iter  20 value 64.307645
## iter  30 value 55.717435
## iter  40 value 46.092851
## iter  50 value 36.850424
## iter  60 value 31.881423
## iter  70 value 28.986970
## iter  80 value 26.924289
## iter  90 value 24.094264
## iter 100 value 21.240172
## final  value 21.240172 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 834.710985 
## iter  10 value 127.168822
## iter  20 value 85.184628
## iter  30 value 61.542006
## iter  40 value 45.856524
## iter  50 value 40.898109
## iter  60 value 38.480930
## iter  70 value 36.039392
## iter  80 value 34.151811
## iter  90 value 32.332708
## iter 100 value 31.752769
## final  value 31.752769 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 664.449830 
## iter  10 value 124.799228
## iter  20 value 77.723107
## iter  30 value 56.761253
## iter  40 value 30.644267
## iter  50 value 21.380753
## iter  60 value 18.937659
## iter  70 value 17.362614
## iter  80 value 16.852073
## iter  90 value 16.255578
## iter 100 value 15.937048
## final  value 15.937048 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1592.161139 
## iter  10 value 164.560348
## iter  20 value 104.717859
## iter  30 value 75.629775
## iter  40 value 60.603570
## iter  50 value 51.916972
## iter  60 value 45.118340
## iter  70 value 42.494433
## iter  80 value 41.767296
## iter  90 value 41.486750
## iter 100 value 41.370826
## final  value 41.370826 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 658.840474 
## iter  10 value 92.835016
## iter  20 value 57.967436
## iter  30 value 39.847065
## iter  40 value 28.541084
## iter  50 value 19.711331
## iter  60 value 18.196322
## iter  70 value 17.512934
## iter  80 value 17.208259
## iter  90 value 17.038975
## iter 100 value 16.863768
## final  value 16.863768 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 740.790099 
## iter  10 value 130.890688
## iter  20 value 79.985647
## iter  30 value 62.244400
## iter  40 value 56.740953
## iter  50 value 55.708515
## iter  60 value 54.136965
## iter  70 value 52.649451
## iter  80 value 50.413778
## iter  90 value 46.967873
## iter 100 value 46.382536
## final  value 46.382536 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 661.710708 
## iter  10 value 120.271986
## iter  20 value 77.893136
## iter  30 value 56.953546
## iter  40 value 42.383248
## iter  50 value 32.557554
## iter  60 value 27.393857
## iter  70 value 24.046864
## iter  80 value 18.932480
## iter  90 value 16.623584
## iter 100 value 15.376632
## final  value 15.376632 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 737.601993 
## iter  10 value 141.616238
## iter  20 value 71.575221
## iter  30 value 45.143917
## iter  40 value 26.726379
## iter  50 value 24.268134
## iter  60 value 22.734634
## iter  70 value 19.502081
## iter  80 value 18.438713
## iter  90 value 17.790368
## iter 100 value 17.246619
## final  value 17.246619 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1014.605552 
## iter  10 value 137.495706
## iter  20 value 69.929929
## iter  30 value 48.537727
## iter  40 value 33.688979
## iter  50 value 28.207427
## iter  60 value 24.156475
## iter  70 value 21.027962
## iter  80 value 15.055694
## iter  90 value 13.669044
## iter 100 value 13.149689
## final  value 13.149689 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 871.014746 
## iter  10 value 153.046248
## iter  20 value 103.329357
## iter  30 value 71.169208
## iter  40 value 52.067732
## iter  50 value 37.631827
## iter  60 value 28.452878
## iter  70 value 25.374398
## iter  80 value 21.904702
## iter  90 value 19.984635
## iter 100 value 18.828913
## final  value 18.828913 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1231.712310 
## iter  10 value 124.340879
## iter  20 value 79.174105
## iter  30 value 61.011413
## iter  40 value 46.341594
## iter  50 value 34.436822
## iter  60 value 29.885607
## iter  70 value 28.270969
## iter  80 value 26.776261
## iter  90 value 25.893673
## iter 100 value 25.297361
## final  value 25.297361 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1067.732725 
## iter  10 value 121.246722
## iter  20 value 86.363111
## iter  30 value 61.513014
## iter  40 value 52.115629
## iter  50 value 42.957900
## iter  60 value 40.823479
## iter  70 value 39.773968
## iter  80 value 38.647849
## iter  90 value 37.509098
## iter 100 value 36.529919
## final  value 36.529919 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1198.722340 
## iter  10 value 123.054966
## iter  20 value 76.034056
## iter  30 value 48.762422
## iter  40 value 33.329886
## iter  50 value 26.896495
## iter  60 value 24.595371
## iter  70 value 22.857625
## iter  80 value 22.009065
## iter  90 value 21.436909
## iter 100 value 20.769204
## final  value 20.769204 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1411.452124 
## iter  10 value 144.093852
## iter  20 value 88.983078
## iter  30 value 57.855651
## iter  40 value 43.138707
## iter  50 value 33.785251
## iter  60 value 28.841188
## iter  70 value 21.597457
## iter  80 value 18.384090
## iter  90 value 16.033748
## iter 100 value 14.288980
## final  value 14.288980 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 864.691051 
## iter  10 value 127.722056
## iter  20 value 92.104598
## iter  30 value 65.698924
## iter  40 value 47.711643
## iter  50 value 35.970903
## iter  60 value 28.994867
## iter  70 value 26.039643
## iter  80 value 24.357933
## iter  90 value 23.114614
## iter 100 value 20.505011
## final  value 20.505011 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1398.414805 
## iter  10 value 169.528108
## iter  20 value 116.338215
## iter  30 value 86.821256
## iter  40 value 58.546813
## iter  50 value 48.161525
## iter  60 value 45.327030
## iter  70 value 41.706457
## iter  80 value 39.701561
## iter  90 value 38.670141
## iter 100 value 36.139023
## final  value 36.139023 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 728.908585 
## iter  10 value 123.353744
## iter  20 value 79.581771
## iter  30 value 52.217499
## iter  40 value 39.798616
## iter  50 value 33.176839
## iter  60 value 30.031678
## iter  70 value 25.477489
## iter  80 value 24.346198
## iter  90 value 22.903675
## iter 100 value 22.439879
## final  value 22.439879 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 942.162004 
## iter  10 value 96.157048
## iter  20 value 66.482679
## iter  30 value 39.851736
## iter  40 value 24.092600
## iter  50 value 18.495097
## iter  60 value 16.481115
## iter  70 value 13.641759
## iter  80 value 13.149060
## iter  90 value 12.854124
## iter 100 value 12.565997
## final  value 12.565997 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1229.928072 
## iter  10 value 164.711354
## iter  20 value 89.963466
## iter  30 value 58.364064
## iter  40 value 40.427912
## iter  50 value 32.615547
## iter  60 value 30.988605
## iter  70 value 29.492948
## iter  80 value 28.473591
## iter  90 value 27.518254
## iter 100 value 26.578047
## final  value 26.578047 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1223.600138 
## iter  10 value 98.733896
## iter  20 value 66.693741
## iter  30 value 39.539255
## iter  40 value 28.170756
## iter  50 value 24.899689
## iter  60 value 16.961827
## iter  70 value 15.166721
## iter  80 value 13.744446
## iter  90 value 12.068941
## iter 100 value 11.567918
## final  value 11.567918 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 796.157277 
## iter  10 value 111.673348
## iter  20 value 67.424350
## iter  30 value 37.514418
## iter  40 value 28.710776
## iter  50 value 25.446332
## iter  60 value 21.252969
## iter  70 value 19.113033
## iter  80 value 18.253161
## iter  90 value 17.657057
## iter 100 value 13.356019
## final  value 13.356019 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1314.868201 
## iter  10 value 143.210820
## iter  20 value 85.321689
## iter  30 value 66.313973
## iter  40 value 58.187916
## iter  50 value 55.923846
## iter  60 value 55.255012
## iter  70 value 54.324853
## iter  80 value 50.239850
## iter  90 value 45.906059
## iter 100 value 44.204768
## final  value 44.204768 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1010.179785 
## iter  10 value 133.010432
## iter  20 value 89.344909
## iter  30 value 68.150519
## iter  40 value 62.556703
## iter  50 value 58.865548
## iter  60 value 55.622642
## iter  70 value 53.333750
## iter  80 value 52.339969
## iter  90 value 51.970811
## iter 100 value 45.405095
## final  value 45.405095 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 712.812558 
## iter  10 value 142.839054
## iter  20 value 102.689080
## iter  30 value 74.182589
## iter  40 value 53.475853
## iter  50 value 46.488098
## iter  60 value 43.250217
## iter  70 value 41.953726
## iter  80 value 40.953770
## iter  90 value 40.307168
## iter 100 value 39.706732
## final  value 39.706732 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1362.144482 
## iter  10 value 129.820071
## iter  20 value 97.703730
## iter  30 value 65.476447
## iter  40 value 48.226837
## iter  50 value 34.210065
## iter  60 value 30.546027
## iter  70 value 29.310343
## iter  80 value 28.539819
## iter  90 value 27.970128
## iter 100 value 26.707062
## final  value 26.707062 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1647.066492 
## iter  10 value 119.445229
## iter  20 value 74.570440
## iter  30 value 57.748380
## iter  40 value 51.070737
## iter  50 value 48.629215
## iter  60 value 46.901972
## iter  70 value 45.474681
## iter  80 value 44.048030
## iter  90 value 43.478117
## iter 100 value 43.184989
## final  value 43.184989 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1072.690588 
## iter  10 value 143.390131
## iter  20 value 83.539518
## iter  30 value 52.669284
## iter  40 value 41.588897
## iter  50 value 38.343004
## iter  60 value 35.338834
## iter  70 value 33.093810
## iter  80 value 32.059026
## iter  90 value 31.615300
## iter 100 value 31.460972
## final  value 31.460972 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1115.722458 
## iter  10 value 186.067705
## iter  20 value 136.822786
## iter  30 value 101.475055
## iter  40 value 80.888993
## iter  50 value 68.739762
## iter  60 value 63.201361
## iter  70 value 60.394465
## iter  80 value 56.926682
## iter  90 value 54.772279
## iter 100 value 53.259534
## final  value 53.259534 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1347.348351 
## iter  10 value 119.283538
## iter  20 value 75.391144
## iter  30 value 57.638563
## iter  40 value 41.046020
## iter  50 value 30.351843
## iter  60 value 18.921362
## iter  70 value 13.689574
## iter  80 value 6.287260
## iter  90 value 5.103203
## iter 100 value 4.508947
## final  value 4.508947 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1637.699780 
## iter  10 value 93.638853
## iter  20 value 42.007632
## iter  30 value 23.556351
## iter  40 value 19.289690
## iter  50 value 17.838540
## iter  60 value 16.600745
## iter  70 value 14.566365
## iter  80 value 13.493842
## iter  90 value 11.790590
## iter 100 value 10.801087
## final  value 10.801087 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 728.407297 
## iter  10 value 133.120478
## iter  20 value 73.401981
## iter  30 value 37.103038
## iter  40 value 30.198920
## iter  50 value 26.501968
## iter  60 value 22.614717
## iter  70 value 20.936010
## iter  80 value 19.739772
## iter  90 value 17.991408
## iter 100 value 17.153825
## final  value 17.153825 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1660.138021 
## iter  10 value 86.850475
## iter  20 value 40.131254
## iter  30 value 24.529881
## iter  40 value 19.719543
## iter  50 value 18.096355
## iter  60 value 14.372810
## iter  70 value 12.140374
## iter  80 value 11.799675
## iter  90 value 11.318708
## iter 100 value 10.892758
## final  value 10.892758 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 871.461883 
## iter  10 value 120.315946
## iter  20 value 72.137346
## iter  30 value 48.451205
## iter  40 value 38.348391
## iter  50 value 35.533473
## iter  60 value 34.116516
## iter  70 value 32.511124
## iter  80 value 31.569171
## iter  90 value 31.154358
## iter 100 value 30.677204
## final  value 30.677204 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 846.047295 
## iter  10 value 123.840820
## iter  20 value 90.880054
## iter  30 value 68.885604
## iter  40 value 43.112193
## iter  50 value 34.745372
## iter  60 value 30.508083
## iter  70 value 27.979842
## iter  80 value 26.838072
## iter  90 value 25.393339
## iter 100 value 24.622412
## final  value 24.622412 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 628.828318 
## iter  10 value 77.286084
## iter  20 value 48.199439
## iter  30 value 36.251258
## iter  40 value 32.096722
## iter  50 value 30.489283
## iter  60 value 29.759831
## iter  70 value 29.184044
## iter  80 value 28.409858
## iter  90 value 27.708832
## iter 100 value 26.919205
## final  value 26.919205 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1110.929023 
## iter  10 value 97.833828
## iter  20 value 59.714513
## iter  30 value 37.312691
## iter  40 value 26.020965
## iter  50 value 22.571155
## iter  60 value 20.739695
## iter  70 value 18.693733
## iter  80 value 18.143173
## iter  90 value 15.453138
## iter 100 value 14.508451
## final  value 14.508451 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 692.305238 
## iter  10 value 74.277171
## iter  20 value 42.665256
## iter  30 value 27.531811
## iter  40 value 18.033506
## iter  50 value 13.377464
## iter  60 value 10.768733
## iter  70 value 9.922330
## iter  80 value 8.935259
## iter  90 value 6.793743
## iter 100 value 5.969667
## final  value 5.969667 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 865.840591 
## iter  10 value 119.279535
## iter  20 value 76.494190
## iter  30 value 60.296135
## iter  40 value 47.225912
## iter  50 value 32.705415
## iter  60 value 25.094455
## iter  70 value 20.618936
## iter  80 value 17.220227
## iter  90 value 15.619106
## iter 100 value 14.565029
## final  value 14.565029 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1501.221181 
## iter  10 value 90.243672
## iter  20 value 49.606825
## iter  30 value 32.177726
## iter  40 value 23.841478
## iter  50 value 21.674666
## iter  60 value 20.289294
## iter  70 value 19.225893
## iter  80 value 18.756494
## iter  90 value 18.493218
## iter 100 value 18.383552
## final  value 18.383552 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1308.206121 
## iter  10 value 125.039225
## iter  20 value 72.678816
## iter  30 value 49.445617
## iter  40 value 34.816827
## iter  50 value 29.200805
## iter  60 value 27.416924
## iter  70 value 26.113259
## iter  80 value 25.510193
## iter  90 value 25.139640
## iter 100 value 24.812779
## final  value 24.812779 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1143.929649 
## iter  10 value 143.600564
## iter  20 value 70.976472
## iter  30 value 57.261732
## iter  40 value 52.399366
## iter  50 value 48.218394
## iter  60 value 45.377548
## iter  70 value 43.991056
## iter  80 value 42.986607
## iter  90 value 41.752270
## iter 100 value 38.786734
## final  value 38.786734 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 896.163096 
## iter  10 value 107.071988
## iter  20 value 65.509945
## iter  30 value 47.240462
## iter  40 value 43.119774
## iter  50 value 40.659970
## iter  60 value 39.227308
## iter  70 value 37.903895
## iter  80 value 36.504641
## iter  90 value 33.462955
## iter 100 value 32.734777
## final  value 32.734777 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1096.307033 
## iter  10 value 145.028072
## iter  20 value 96.442753
## iter  30 value 71.357545
## iter  40 value 64.320857
## iter  50 value 52.558285
## iter  60 value 43.846051
## iter  70 value 38.489145
## iter  80 value 33.753895
## iter  90 value 30.239030
## iter 100 value 29.133842
## final  value 29.133842 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 899.420602 
## iter  10 value 120.334376
## iter  20 value 85.229808
## iter  30 value 55.051945
## iter  40 value 34.973008
## iter  50 value 27.270935
## iter  60 value 25.262963
## iter  70 value 24.492811
## iter  80 value 22.971446
## iter  90 value 22.016184
## iter 100 value 20.349750
## final  value 20.349750 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 862.272745 
## iter  10 value 129.200818
## iter  20 value 82.523982
## iter  30 value 65.931438
## iter  40 value 53.093163
## iter  50 value 47.545358
## iter  60 value 36.986450
## iter  70 value 30.417869
## iter  80 value 27.730118
## iter  90 value 25.837171
## iter 100 value 19.724046
## final  value 19.724046 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1262.712244 
## iter  10 value 170.519084
## iter  20 value 111.090633
## iter  30 value 91.338907
## iter  40 value 77.028066
## iter  50 value 60.481907
## iter  60 value 52.814655
## iter  70 value 49.292725
## iter  80 value 44.590089
## iter  90 value 42.088867
## iter 100 value 39.287090
## final  value 39.287090 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 798.439177 
## iter  10 value 168.436064
## iter  20 value 120.249858
## iter  30 value 91.240090
## iter  40 value 80.800660
## iter  50 value 72.253885
## iter  60 value 66.382716
## iter  70 value 61.034528
## iter  80 value 55.573381
## iter  90 value 52.465924
## iter 100 value 48.297932
## final  value 48.297932 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 940.841811 
## iter  10 value 111.766918
## iter  20 value 61.567875
## iter  30 value 34.897023
## iter  40 value 21.084618
## iter  50 value 11.954614
## iter  60 value 9.862225
## iter  70 value 8.712313
## iter  80 value 7.508629
## iter  90 value 6.624663
## iter 100 value 6.099956
## final  value 6.099956 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1282.493396 
## iter  10 value 101.841879
## iter  20 value 63.923915
## iter  30 value 36.147552
## iter  40 value 21.458900
## iter  50 value 17.867202
## iter  60 value 16.317962
## iter  70 value 15.547577
## iter  80 value 13.444305
## iter  90 value 10.598989
## iter 100 value 7.692040
## final  value 7.692040 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1530.206838 
## iter  10 value 100.406608
## iter  20 value 49.357032
## iter  30 value 23.736073
## iter  40 value 14.756649
## iter  50 value 11.159552
## iter  60 value 9.639123
## iter  70 value 9.175546
## iter  80 value 7.747127
## iter  90 value 7.165190
## iter 100 value 6.840433
## final  value 6.840433 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1812.646316 
## iter  10 value 120.361592
## iter  20 value 66.290774
## iter  30 value 49.134838
## iter  40 value 37.693338
## iter  50 value 33.565908
## iter  60 value 26.467636
## iter  70 value 24.839869
## iter  80 value 23.310029
## iter  90 value 21.716260
## iter 100 value 21.058672
## final  value 21.058672 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 931.605128 
## iter  10 value 114.527683
## iter  20 value 70.762005
## iter  30 value 44.975910
## iter  40 value 29.895399
## iter  50 value 25.925048
## iter  60 value 24.531652
## iter  70 value 23.479045
## iter  80 value 22.724173
## iter  90 value 22.421511
## iter 100 value 22.245268
## final  value 22.245268 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1021.265801 
## iter  10 value 97.580959
## iter  20 value 57.172843
## iter  30 value 38.039262
## iter  40 value 25.397739
## iter  50 value 17.770171
## iter  60 value 13.111429
## iter  70 value 11.658329
## iter  80 value 10.999806
## iter  90 value 10.744237
## iter 100 value 10.598704
## final  value 10.598704 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 849.686325 
## iter  10 value 114.383242
## iter  20 value 71.375485
## iter  30 value 52.966841
## iter  40 value 42.444074
## iter  50 value 38.558806
## iter  60 value 36.807082
## iter  70 value 34.954464
## iter  80 value 33.368403
## iter  90 value 32.965106
## iter 100 value 32.790744
## final  value 32.790744 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 767.421019 
## iter  10 value 136.452127
## iter  20 value 90.684739
## iter  30 value 62.452015
## iter  40 value 44.117585
## iter  50 value 37.847176
## iter  60 value 34.729302
## iter  70 value 32.748945
## iter  80 value 31.786345
## iter  90 value 30.490919
## iter 100 value 29.937210
## final  value 29.937210 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1065.425972 
## iter  10 value 105.486648
## iter  20 value 63.663382
## iter  30 value 46.256866
## iter  40 value 35.632006
## iter  50 value 27.220122
## iter  60 value 18.268626
## iter  70 value 16.953582
## iter  80 value 15.309988
## iter  90 value 14.077036
## iter 100 value 13.325719
## final  value 13.325719 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1201.120818 
## iter  10 value 95.236611
## iter  20 value 60.631355
## iter  30 value 38.665907
## iter  40 value 20.786122
## iter  50 value 16.324100
## iter  60 value 15.358073
## iter  70 value 14.879513
## iter  80 value 14.646307
## iter  90 value 14.475665
## iter 100 value 14.212426
## final  value 14.212426 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 898.024588 
## iter  10 value 159.474578
## iter  20 value 104.700915
## iter  30 value 61.034481
## iter  40 value 40.751900
## iter  50 value 37.259461
## iter  60 value 35.518504
## iter  70 value 31.488453
## iter  80 value 29.584904
## iter  90 value 28.487693
## iter 100 value 28.002595
## final  value 28.002595 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 916.602804 
## iter  10 value 121.374843
## iter  20 value 58.766626
## iter  30 value 42.628090
## iter  40 value 32.931735
## iter  50 value 29.030767
## iter  60 value 27.663961
## iter  70 value 26.623861
## iter  80 value 24.371593
## iter  90 value 23.190890
## iter 100 value 22.273309
## final  value 22.273309 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 941.316291 
## iter  10 value 131.753190
## iter  20 value 73.223011
## iter  30 value 55.045012
## iter  40 value 50.012444
## iter  50 value 44.484817
## iter  60 value 36.826407
## iter  70 value 31.381545
## iter  80 value 25.050219
## iter  90 value 23.539228
## iter 100 value 22.959688
## final  value 22.959688 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 887.165549 
## iter  10 value 111.193859
## iter  20 value 58.903292
## iter  30 value 34.786451
## iter  40 value 26.388456
## iter  50 value 22.977795
## iter  60 value 20.800519
## iter  70 value 19.248089
## iter  80 value 18.471766
## iter  90 value 17.240999
## iter 100 value 16.577690
## final  value 16.577690 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 795.726549 
## iter  10 value 115.489906
## iter  20 value 81.838399
## iter  30 value 61.529701
## iter  40 value 47.171221
## iter  50 value 36.136315
## iter  60 value 28.702235
## iter  70 value 25.750239
## iter  80 value 22.983363
## iter  90 value 20.366156
## iter 100 value 19.271689
## final  value 19.271689 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 752.502721 
## iter  10 value 131.553078
## iter  20 value 72.965168
## iter  30 value 50.875351
## iter  40 value 36.816580
## iter  50 value 32.689140
## iter  60 value 29.641665
## iter  70 value 28.571877
## iter  80 value 25.175326
## iter  90 value 22.848996
## iter 100 value 21.915872
## final  value 21.915872 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 853.398714 
## iter  10 value 158.113890
## iter  20 value 113.476642
## iter  30 value 87.459649
## iter  40 value 69.840485
## iter  50 value 57.378227
## iter  60 value 49.353787
## iter  70 value 46.561554
## iter  80 value 43.342047
## iter  90 value 40.175946
## iter 100 value 38.108514
## final  value 38.108514 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 754.907155 
## iter  10 value 162.592426
## iter  20 value 86.449380
## iter  30 value 51.346072
## iter  40 value 38.950873
## iter  50 value 35.501842
## iter  60 value 34.445598
## iter  70 value 33.532094
## iter  80 value 32.413430
## iter  90 value 31.226408
## iter 100 value 29.425260
## final  value 29.425260 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1119.140838 
## iter  10 value 94.816320
## iter  20 value 53.517146
## iter  30 value 35.292057
## iter  40 value 24.538745
## iter  50 value 22.605490
## iter  60 value 21.555546
## iter  70 value 20.812005
## iter  80 value 19.527575
## iter  90 value 17.862671
## iter 100 value 17.197654
## final  value 17.197654 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1225.152028 
## iter  10 value 114.634250
## iter  20 value 63.135035
## iter  30 value 39.580916
## iter  40 value 21.435471
## iter  50 value 14.227098
## iter  60 value 12.737268
## iter  70 value 12.060089
## iter  80 value 11.512655
## iter  90 value 11.251570
## iter 100 value 11.005232
## final  value 11.005232 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1019.739879 
## iter  10 value 84.452797
## iter  20 value 49.759049
## iter  30 value 30.069126
## iter  40 value 23.297462
## iter  50 value 21.701061
## iter  60 value 20.622922
## iter  70 value 20.352875
## iter  80 value 20.205248
## iter  90 value 20.048664
## iter 100 value 19.972379
## final  value 19.972379 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1194.583709 
## iter  10 value 118.319030
## iter  20 value 70.748769
## iter  30 value 52.572900
## iter  40 value 41.214218
## iter  50 value 36.736221
## iter  60 value 34.094023
## iter  70 value 27.517380
## iter  80 value 22.108935
## iter  90 value 19.969107
## iter 100 value 19.421282
## final  value 19.421282 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1468.978937 
## iter  10 value 141.632813
## iter  20 value 93.312119
## iter  30 value 51.699441
## iter  40 value 31.506666
## iter  50 value 22.990156
## iter  60 value 17.238789
## iter  70 value 16.156863
## iter  80 value 15.637024
## iter  90 value 14.111559
## iter 100 value 9.244880
## final  value 9.244880 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 763.070034 
## iter  10 value 134.960775
## iter  20 value 98.363975
## iter  30 value 85.822429
## iter  40 value 65.240105
## iter  50 value 49.328685
## iter  60 value 41.768471
## iter  70 value 35.735788
## iter  80 value 29.909148
## iter  90 value 28.024585
## iter 100 value 26.280125
## final  value 26.280125 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 721.281387 
## iter  10 value 146.770835
## iter  20 value 84.209554
## iter  30 value 52.590843
## iter  40 value 34.384807
## iter  50 value 21.584486
## iter  60 value 16.958125
## iter  70 value 15.669977
## iter  80 value 14.455174
## iter  90 value 12.592604
## iter 100 value 11.795633
## final  value 11.795633 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 921.933717 
## iter  10 value 120.451668
## iter  20 value 70.589396
## iter  30 value 45.387666
## iter  40 value 32.238940
## iter  50 value 23.787571
## iter  60 value 21.174211
## iter  70 value 19.365472
## iter  80 value 18.732415
## iter  90 value 18.127762
## iter 100 value 17.710571
## final  value 17.710571 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 664.250340 
## iter  10 value 118.694032
## iter  20 value 79.227307
## iter  30 value 49.141582
## iter  40 value 38.776847
## iter  50 value 35.543564
## iter  60 value 32.755293
## iter  70 value 30.424756
## iter  80 value 29.308696
## iter  90 value 28.754122
## iter 100 value 28.413590
## final  value 28.413590 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 781.829146 
## iter  10 value 145.870947
## iter  20 value 88.260830
## iter  30 value 53.386900
## iter  40 value 37.056700
## iter  50 value 29.588793
## iter  60 value 23.295574
## iter  70 value 22.280840
## iter  80 value 21.716654
## iter  90 value 21.083423
## iter 100 value 20.567025
## final  value 20.567025 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 870.252807 
## iter  10 value 150.992695
## iter  20 value 94.280299
## iter  30 value 61.341700
## iter  40 value 52.404610
## iter  50 value 44.450609
## iter  60 value 37.417297
## iter  70 value 33.894724
## iter  80 value 32.824388
## iter  90 value 31.784834
## iter 100 value 31.174431
## final  value 31.174431 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 688.246258 
## iter  10 value 87.090137
## iter  20 value 46.056525
## iter  30 value 28.894289
## iter  40 value 18.873204
## iter  50 value 14.796399
## iter  60 value 13.269203
## iter  70 value 12.562626
## iter  80 value 12.149038
## iter  90 value 11.507870
## iter 100 value 10.356999
## final  value 10.356999 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 683.716594 
## iter  10 value 143.857458
## iter  20 value 93.593795
## iter  30 value 58.373753
## iter  40 value 44.236232
## iter  50 value 39.488205
## iter  60 value 36.370327
## iter  70 value 34.698047
## iter  80 value 32.565047
## iter  90 value 31.406025
## iter 100 value 30.155002
## final  value 30.155002 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1203.399468 
## iter  10 value 99.016898
## iter  20 value 52.956141
## iter  30 value 31.689742
## iter  40 value 24.122921
## iter  50 value 20.900680
## iter  60 value 18.896855
## iter  70 value 17.483121
## iter  80 value 16.800145
## iter  90 value 16.345478
## iter 100 value 15.537424
## final  value 15.537424 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1089.022902 
## iter  10 value 97.671864
## iter  20 value 63.069301
## iter  30 value 42.346955
## iter  40 value 31.972876
## iter  50 value 27.156536
## iter  60 value 24.340208
## iter  70 value 23.583804
## iter  80 value 22.707498
## iter  90 value 22.118885
## iter 100 value 21.952941
## final  value 21.952941 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1305.976172 
## iter  10 value 103.555986
## iter  20 value 65.961640
## iter  30 value 44.981279
## iter  40 value 26.282511
## iter  50 value 14.511588
## iter  60 value 11.022855
## iter  70 value 10.133573
## iter  80 value 9.610681
## iter  90 value 9.322837
## iter 100 value 9.029470
## final  value 9.029470 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 991.458467 
## iter  10 value 130.876763
## iter  20 value 75.434423
## iter  30 value 54.424076
## iter  40 value 42.025516
## iter  50 value 32.942690
## iter  60 value 26.626988
## iter  70 value 25.762304
## iter  80 value 25.130116
## iter  90 value 24.613679
## iter 100 value 24.263291
## final  value 24.263291 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 662.284468 
## iter  10 value 133.752578
## iter  20 value 73.808282
## iter  30 value 52.994556
## iter  40 value 43.009623
## iter  50 value 39.559617
## iter  60 value 38.285915
## iter  70 value 37.497296
## iter  80 value 36.912195
## iter  90 value 35.746142
## iter 100 value 33.458102
## final  value 33.458102 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 724.841079 
## iter  10 value 95.818843
## iter  20 value 56.716256
## iter  30 value 42.170911
## iter  40 value 38.161130
## iter  50 value 36.222270
## iter  60 value 34.092737
## iter  70 value 33.198486
## iter  80 value 31.546566
## iter  90 value 29.106203
## iter 100 value 28.410253
## final  value 28.410253 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 824.557607 
## iter  10 value 98.009565
## iter  20 value 70.520849
## iter  30 value 52.132048
## iter  40 value 42.921376
## iter  50 value 34.506443
## iter  60 value 29.848757
## iter  70 value 27.183218
## iter  80 value 25.089127
## iter  90 value 22.740110
## iter 100 value 20.391329
## final  value 20.391329 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 2022.606361 
## iter  10 value 109.716820
## iter  20 value 66.396879
## iter  30 value 36.300732
## iter  40 value 21.539916
## iter  50 value 17.907803
## iter  60 value 16.612498
## iter  70 value 15.817623
## iter  80 value 15.049617
## iter  90 value 14.444036
## iter 100 value 13.935672
## final  value 13.935672 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1066.472388 
## iter  10 value 112.653372
## iter  20 value 77.050470
## iter  30 value 53.970940
## iter  40 value 33.125792
## iter  50 value 20.117520
## iter  60 value 16.593242
## iter  70 value 15.226365
## iter  80 value 13.910487
## iter  90 value 13.476391
## iter 100 value 13.196194
## final  value 13.196194 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1157.255627 
## iter  10 value 110.125060
## iter  20 value 81.328043
## iter  30 value 57.490138
## iter  40 value 44.217096
## iter  50 value 38.091300
## iter  60 value 36.137902
## iter  70 value 34.936360
## iter  80 value 32.991075
## iter  90 value 31.286909
## iter 100 value 30.583300
## final  value 30.583300 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 814.228646 
## iter  10 value 116.314492
## iter  20 value 73.891566
## iter  30 value 46.041182
## iter  40 value 37.078844
## iter  50 value 30.126540
## iter  60 value 28.698207
## iter  70 value 25.015459
## iter  80 value 22.650290
## iter  90 value 21.795239
## iter 100 value 21.031547
## final  value 21.031547 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 641.755395 
## iter  10 value 107.783069
## iter  20 value 62.694012
## iter  30 value 27.179509
## iter  40 value 15.284554
## iter  50 value 12.080894
## iter  60 value 11.182048
## iter  70 value 10.697577
## iter  80 value 10.290341
## iter  90 value 9.893395
## iter 100 value 9.728276
## final  value 9.728276 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 810.879395 
## iter  10 value 105.104270
## iter  20 value 53.256632
## iter  30 value 36.293382
## iter  40 value 31.328315
## iter  50 value 27.500406
## iter  60 value 24.637682
## iter  70 value 21.040870
## iter  80 value 20.070257
## iter  90 value 19.338249
## iter 100 value 18.284229
## final  value 18.284229 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 912.845701 
## iter  10 value 111.649065
## iter  20 value 72.363558
## iter  30 value 49.007830
## iter  40 value 35.494446
## iter  50 value 29.651193
## iter  60 value 23.597540
## iter  70 value 20.890265
## iter  80 value 17.184138
## iter  90 value 16.249433
## iter 100 value 15.522910
## final  value 15.522910 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 859.855123 
## iter  10 value 133.170522
## iter  20 value 101.843301
## iter  30 value 73.197758
## iter  40 value 57.192593
## iter  50 value 45.437517
## iter  60 value 39.430364
## iter  70 value 37.672237
## iter  80 value 36.444842
## iter  90 value 35.556495
## iter 100 value 35.087240
## final  value 35.087240 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 841.489722 
## iter  10 value 148.229915
## iter  20 value 102.596595
## iter  30 value 78.118038
## iter  40 value 64.021496
## iter  50 value 57.557266
## iter  60 value 54.582263
## iter  70 value 49.728089
## iter  80 value 47.787107
## iter  90 value 46.408642
## iter 100 value 44.800850
## final  value 44.800850 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1001.048331 
## iter  10 value 92.388537
## iter  20 value 62.235034
## iter  30 value 35.053051
## iter  40 value 28.609670
## iter  50 value 27.115679
## iter  60 value 25.917621
## iter  70 value 24.785498
## iter  80 value 23.406569
## iter  90 value 22.923433
## iter 100 value 22.498866
## final  value 22.498866 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1071.554449 
## iter  10 value 117.202988
## iter  20 value 80.209923
## iter  30 value 47.699256
## iter  40 value 18.598482
## iter  50 value 14.208827
## iter  60 value 12.450779
## iter  70 value 11.023837
## iter  80 value 10.366942
## iter  90 value 10.030868
## iter 100 value 9.612095
## final  value 9.612095 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1056.252578 
## iter  10 value 119.199930
## iter  20 value 79.958178
## iter  30 value 64.582435
## iter  40 value 53.550507
## iter  50 value 44.376390
## iter  60 value 40.905864
## iter  70 value 39.593915
## iter  80 value 38.170205
## iter  90 value 36.279303
## iter 100 value 34.542872
## final  value 34.542872 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1044.446674 
## iter  10 value 139.607614
## iter  20 value 65.721177
## iter  30 value 49.861286
## iter  40 value 41.787978
## iter  50 value 39.938306
## iter  60 value 39.035404
## iter  70 value 37.919465
## iter  80 value 36.930993
## iter  90 value 36.352011
## iter 100 value 35.516183
## final  value 35.516183 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1002.062278 
## iter  10 value 118.252472
## iter  20 value 81.173395
## iter  30 value 55.879545
## iter  40 value 27.815496
## iter  50 value 17.695384
## iter  60 value 14.155204
## iter  70 value 12.111294
## iter  80 value 10.685498
## iter  90 value 9.231034
## iter 100 value 8.610992
## final  value 8.610992 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 972.904208 
## iter  10 value 183.682164
## iter  20 value 120.550851
## iter  30 value 88.559171
## iter  40 value 67.369605
## iter  50 value 57.442585
## iter  60 value 54.765343
## iter  70 value 52.831144
## iter  80 value 51.192601
## iter  90 value 50.272333
## iter 100 value 49.524311
## final  value 49.524311 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 904.286686 
## iter  10 value 127.441318
## iter  20 value 69.044594
## iter  30 value 36.282162
## iter  40 value 18.055610
## iter  50 value 12.440011
## iter  60 value 10.697337
## iter  70 value 8.586964
## iter  80 value 6.710649
## iter  90 value 5.956402
## iter 100 value 5.425592
## final  value 5.425592 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1202.692920 
## iter  10 value 108.570878
## iter  20 value 65.189743
## iter  30 value 40.868007
## iter  40 value 33.201234
## iter  50 value 31.141747
## iter  60 value 30.023620
## iter  70 value 29.110384
## iter  80 value 28.421063
## iter  90 value 26.749617
## iter 100 value 25.778486
## final  value 25.778486 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 960.782608 
## iter  10 value 173.156586
## iter  20 value 79.579263
## iter  30 value 50.199624
## iter  40 value 31.858067
## iter  50 value 18.585926
## iter  60 value 15.885859
## iter  70 value 14.665459
## iter  80 value 14.062664
## iter  90 value 13.805184
## iter 100 value 13.352890
## final  value 13.352890 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 684.577174 
## iter  10 value 122.401211
## iter  20 value 77.442571
## iter  30 value 55.147165
## iter  40 value 43.454927
## iter  50 value 37.228963
## iter  60 value 32.513108
## iter  70 value 30.683014
## iter  80 value 29.406135
## iter  90 value 27.985861
## iter 100 value 26.850702
## final  value 26.850702 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 861.468395 
## iter  10 value 90.985449
## iter  20 value 50.923780
## iter  30 value 33.289523
## iter  40 value 25.907700
## iter  50 value 21.709325
## iter  60 value 19.211409
## iter  70 value 17.254124
## iter  80 value 16.035398
## iter  90 value 15.306749
## iter 100 value 14.850454
## final  value 14.850454 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1908.965842 
## iter  10 value 179.210022
## iter  20 value 125.230340
## iter  30 value 94.149991
## iter  40 value 78.458944
## iter  50 value 62.464990
## iter  60 value 57.047817
## iter  70 value 52.751737
## iter  80 value 45.656772
## iter  90 value 41.470419
## iter 100 value 39.759083
## final  value 39.759083 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 606.271021 
## iter  10 value 85.483079
## iter  20 value 59.152037
## iter  30 value 36.641010
## iter  40 value 25.652377
## iter  50 value 17.109428
## iter  60 value 12.217731
## iter  70 value 10.881898
## iter  80 value 9.415202
## iter  90 value 8.158772
## iter 100 value 5.470687
## final  value 5.470687 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1557.438959 
## iter  10 value 101.129223
## iter  20 value 62.489791
## iter  30 value 42.380419
## iter  40 value 31.329082
## iter  50 value 22.341291
## iter  60 value 20.265774
## iter  70 value 16.606899
## iter  80 value 13.602855
## iter  90 value 12.541088
## iter 100 value 11.777379
## final  value 11.777379 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1635.890842 
## iter  10 value 111.730895
## iter  20 value 68.902895
## iter  30 value 41.074702
## iter  40 value 22.790565
## iter  50 value 14.772947
## iter  60 value 12.566847
## iter  70 value 11.705929
## iter  80 value 11.088096
## iter  90 value 9.555803
## iter 100 value 8.036325
## final  value 8.036325 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 908.013825 
## iter  10 value 115.607695
## iter  20 value 70.442736
## iter  30 value 49.319938
## iter  40 value 33.965930
## iter  50 value 25.679819
## iter  60 value 21.755410
## iter  70 value 19.924497
## iter  80 value 18.544648
## iter  90 value 16.136739
## iter 100 value 13.052856
## final  value 13.052856 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 706.173114 
## iter  10 value 99.590184
## iter  20 value 61.109694
## iter  30 value 43.476816
## iter  40 value 34.174723
## iter  50 value 31.521544
## iter  60 value 28.387292
## iter  70 value 27.240673
## iter  80 value 26.214395
## iter  90 value 24.836844
## iter 100 value 24.252639
## final  value 24.252639 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1030.423615 
## iter  10 value 136.052295
## iter  20 value 85.086523
## iter  30 value 59.996270
## iter  40 value 45.629754
## iter  50 value 35.724522
## iter  60 value 31.717144
## iter  70 value 28.056905
## iter  80 value 25.657131
## iter  90 value 24.671723
## iter 100 value 23.781769
## final  value 23.781769 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 896.891671 
## iter  10 value 85.931232
## iter  20 value 50.815840
## iter  30 value 25.838344
## iter  40 value 12.342132
## iter  50 value 6.478602
## iter  60 value 5.527395
## iter  70 value 5.048454
## iter  80 value 4.794981
## iter  90 value 4.582395
## iter 100 value 3.719606
## final  value 3.719606 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 920.882473 
## iter  10 value 128.421293
## iter  20 value 56.956369
## iter  30 value 28.609821
## iter  40 value 17.572741
## iter  50 value 13.715184
## iter  60 value 12.006518
## iter  70 value 10.651044
## iter  80 value 9.725660
## iter  90 value 8.798785
## iter 100 value 7.725340
## final  value 7.725340 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1319.470784 
## iter  10 value 119.913126
## iter  20 value 73.652904
## iter  30 value 48.045447
## iter  40 value 33.437800
## iter  50 value 25.681683
## iter  60 value 23.118943
## iter  70 value 21.447074
## iter  80 value 19.860353
## iter  90 value 18.231758
## iter 100 value 16.573125
## final  value 16.573125 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1466.682534 
## iter  10 value 91.496479
## iter  20 value 60.020121
## iter  30 value 47.096241
## iter  40 value 37.215877
## iter  50 value 34.080944
## iter  60 value 30.984305
## iter  70 value 30.033093
## iter  80 value 29.348696
## iter  90 value 28.800336
## iter 100 value 25.972569
## final  value 25.972569 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 805.417203 
## iter  10 value 97.321946
## iter  20 value 54.621620
## iter  30 value 32.305792
## iter  40 value 24.619148
## iter  50 value 16.394384
## iter  60 value 11.729778
## iter  70 value 10.366553
## iter  80 value 9.336313
## iter  90 value 8.627564
## iter 100 value 8.002869
## final  value 8.002869 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 851.475954 
## iter  10 value 151.290249
## iter  20 value 88.362331
## iter  30 value 59.792748
## iter  40 value 34.282469
## iter  50 value 20.169370
## iter  60 value 16.682524
## iter  70 value 15.619317
## iter  80 value 14.877721
## iter  90 value 14.467402
## iter 100 value 14.189459
## final  value 14.189459 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 615.790475 
## iter  10 value 97.898882
## iter  20 value 54.325633
## iter  30 value 32.305511
## iter  40 value 17.974487
## iter  50 value 13.039395
## iter  60 value 11.123486
## iter  70 value 10.417952
## iter  80 value 9.858997
## iter  90 value 9.298859
## iter 100 value 8.946353
## final  value 8.946353 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1509.394948 
## iter  10 value 123.627699
## iter  20 value 70.044500
## iter  30 value 42.006429
## iter  40 value 32.091753
## iter  50 value 29.288136
## iter  60 value 28.003162
## iter  70 value 26.724699
## iter  80 value 24.948244
## iter  90 value 24.381515
## iter 100 value 23.690722
## final  value 23.690722 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 679.505577 
## iter  10 value 131.806822
## iter  20 value 84.849270
## iter  30 value 49.621694
## iter  40 value 32.640102
## iter  50 value 24.387275
## iter  60 value 21.574269
## iter  70 value 20.403502
## iter  80 value 19.766342
## iter  90 value 17.602968
## iter 100 value 16.395509
## final  value 16.395509 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 709.660625 
## iter  10 value 116.951360
## iter  20 value 65.454729
## iter  30 value 42.542821
## iter  40 value 28.488046
## iter  50 value 23.192569
## iter  60 value 21.702223
## iter  70 value 20.892383
## iter  80 value 18.063994
## iter  90 value 15.946151
## iter 100 value 14.817083
## final  value 14.817083 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 750.557953 
## iter  10 value 89.986268
## iter  20 value 51.419667
## iter  30 value 30.639056
## iter  40 value 22.924264
## iter  50 value 20.535172
## iter  60 value 19.366810
## iter  70 value 17.505925
## iter  80 value 15.055278
## iter  90 value 12.886787
## iter 100 value 11.447026
## final  value 11.447026 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 905.453702 
## iter  10 value 161.316516
## iter  20 value 108.994396
## iter  30 value 85.572269
## iter  40 value 52.471111
## iter  50 value 40.234279
## iter  60 value 35.430475
## iter  70 value 32.579818
## iter  80 value 30.384275
## iter  90 value 29.205320
## iter 100 value 28.398831
## final  value 28.398831 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 945.112410 
## iter  10 value 109.017126
## iter  20 value 63.820654
## iter  30 value 47.828885
## iter  40 value 43.881112
## iter  50 value 41.265982
## iter  60 value 39.107275
## iter  70 value 38.013326
## iter  80 value 37.280826
## iter  90 value 36.084325
## iter 100 value 33.014897
## final  value 33.014897 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 701.029007 
## iter  10 value 104.584539
## iter  20 value 75.101855
## iter  30 value 53.608681
## iter  40 value 41.581629
## iter  50 value 36.249053
## iter  60 value 30.914690
## iter  70 value 29.259715
## iter  80 value 27.999005
## iter  90 value 26.897532
## iter 100 value 25.670171
## final  value 25.670171 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1108.203338 
## iter  10 value 145.742393
## iter  20 value 93.212812
## iter  30 value 78.509416
## iter  40 value 61.840011
## iter  50 value 55.591504
## iter  60 value 51.346508
## iter  70 value 49.569344
## iter  80 value 45.609536
## iter  90 value 39.250499
## iter 100 value 36.977004
## final  value 36.977004 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1322.366552 
## iter  10 value 128.945286
## iter  20 value 70.069034
## iter  30 value 46.004516
## iter  40 value 32.363670
## iter  50 value 26.419479
## iter  60 value 25.284935
## iter  70 value 24.876055
## iter  80 value 24.626479
## iter  90 value 24.477317
## iter 100 value 24.341113
## final  value 24.341113 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1086.328996 
## iter  10 value 133.772090
## iter  20 value 78.351639
## iter  30 value 47.700994
## iter  40 value 28.385677
## iter  50 value 22.780396
## iter  60 value 21.178813
## iter  70 value 20.091250
## iter  80 value 19.623036
## iter  90 value 19.355051
## iter 100 value 19.220923
## final  value 19.220923 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1172.345416 
## iter  10 value 145.398781
## iter  20 value 93.163851
## iter  30 value 64.685847
## iter  40 value 52.950057
## iter  50 value 42.873577
## iter  60 value 39.219224
## iter  70 value 37.218829
## iter  80 value 36.040670
## iter  90 value 35.022363
## iter 100 value 31.419417
## final  value 31.419417 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 794.023698 
## iter  10 value 119.873837
## iter  20 value 62.700328
## iter  30 value 42.490730
## iter  40 value 34.671472
## iter  50 value 30.172488
## iter  60 value 24.683898
## iter  70 value 22.840456
## iter  80 value 21.769008
## iter  90 value 20.766752
## iter 100 value 19.394473
## final  value 19.394473 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 779.561700 
## iter  10 value 176.853468
## iter  20 value 127.875236
## iter  30 value 93.783145
## iter  40 value 66.787845
## iter  50 value 56.696731
## iter  60 value 53.059596
## iter  70 value 49.529310
## iter  80 value 44.596909
## iter  90 value 42.138641
## iter 100 value 39.919653
## final  value 39.919653 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 974.294325 
## iter  10 value 107.711808
## iter  20 value 65.744264
## iter  30 value 47.731534
## iter  40 value 34.246556
## iter  50 value 27.208716
## iter  60 value 21.704943
## iter  70 value 20.562362
## iter  80 value 19.962625
## iter  90 value 19.502259
## iter 100 value 19.070804
## final  value 19.070804 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 738.728066 
## iter  10 value 98.843418
## iter  20 value 53.793983
## iter  30 value 33.372032
## iter  40 value 21.742704
## iter  50 value 18.206404
## iter  60 value 17.136360
## iter  70 value 16.611056
## iter  80 value 15.968814
## iter  90 value 15.618935
## iter 100 value 14.838105
## final  value 14.838105 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 860.106673 
## iter  10 value 126.699223
## iter  20 value 87.753689
## iter  30 value 67.230127
## iter  40 value 45.832300
## iter  50 value 40.558708
## iter  60 value 37.876605
## iter  70 value 36.770463
## iter  80 value 35.906088
## iter  90 value 34.704445
## iter 100 value 34.004996
## final  value 34.004996 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 879.128930 
## iter  10 value 166.148863
## iter  20 value 111.616956
## iter  30 value 71.317646
## iter  40 value 52.555036
## iter  50 value 47.359306
## iter  60 value 44.370761
## iter  70 value 42.190259
## iter  80 value 41.417687
## iter  90 value 41.118276
## iter 100 value 40.881899
## final  value 40.881899 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 814.438695 
## iter  10 value 115.243331
## iter  20 value 81.804745
## iter  30 value 59.343955
## iter  40 value 45.580311
## iter  50 value 41.440367
## iter  60 value 38.386861
## iter  70 value 34.980865
## iter  80 value 31.078791
## iter  90 value 29.093738
## iter 100 value 28.166603
## final  value 28.166603 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 688.821409 
## iter  10 value 85.223452
## iter  20 value 51.125535
## iter  30 value 23.433876
## iter  40 value 11.503869
## iter  50 value 9.312024
## iter  60 value 6.997757
## iter  70 value 6.236421
## iter  80 value 4.358210
## iter  90 value 3.831434
## iter 100 value 3.432779
## final  value 3.432779 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 952.937947 
## iter  10 value 93.487024
## iter  20 value 60.468822
## iter  30 value 51.989882
## iter  40 value 36.773004
## iter  50 value 24.114314
## iter  60 value 17.632055
## iter  70 value 16.197782
## iter  80 value 15.543957
## iter  90 value 15.130779
## iter 100 value 14.774496
## final  value 14.774496 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 676.755007 
## iter  10 value 111.294937
## iter  20 value 69.226032
## iter  30 value 47.443532
## iter  40 value 31.062552
## iter  50 value 27.927549
## iter  60 value 25.757141
## iter  70 value 24.195460
## iter  80 value 23.278334
## iter  90 value 22.301735
## iter 100 value 21.076605
## final  value 21.076605 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 921.136138 
## iter  10 value 81.290153
## iter  20 value 45.778470
## iter  30 value 27.834997
## iter  40 value 16.169381
## iter  50 value 11.235494
## iter  60 value 9.698085
## iter  70 value 8.684426
## iter  80 value 7.744572
## iter  90 value 6.681185
## iter 100 value 4.450008
## final  value 4.450008 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1246.345691 
## iter  10 value 134.888969
## iter  20 value 76.980518
## iter  30 value 55.352672
## iter  40 value 38.378309
## iter  50 value 27.275399
## iter  60 value 24.696362
## iter  70 value 23.299497
## iter  80 value 22.416923
## iter  90 value 21.926621
## iter 100 value 19.917526
## final  value 19.917526 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 843.899607 
## iter  10 value 137.126248
## iter  20 value 90.254206
## iter  30 value 64.229544
## iter  40 value 54.261105
## iter  50 value 52.019945
## iter  60 value 50.475873
## iter  70 value 48.981034
## iter  80 value 47.535989
## iter  90 value 45.748808
## iter 100 value 44.606377
## final  value 44.606377 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 598.159958 
## iter  10 value 136.521191
## iter  20 value 70.742628
## iter  30 value 50.749757
## iter  40 value 43.176478
## iter  50 value 39.348895
## iter  60 value 36.396420
## iter  70 value 31.051244
## iter  80 value 28.925212
## iter  90 value 27.298706
## iter 100 value 26.344517
## final  value 26.344517 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 898.954364 
## iter  10 value 115.707143
## iter  20 value 76.770116
## iter  30 value 51.930028
## iter  40 value 40.379849
## iter  50 value 34.510876
## iter  60 value 32.327835
## iter  70 value 30.677074
## iter  80 value 26.733247
## iter  90 value 25.427399
## iter 100 value 24.828597
## final  value 24.828597 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1502.592041 
## iter  10 value 118.920083
## iter  20 value 77.207838
## iter  30 value 53.209185
## iter  40 value 30.996259
## iter  50 value 25.010345
## iter  60 value 22.295075
## iter  70 value 19.862279
## iter  80 value 17.115841
## iter  90 value 15.909994
## iter 100 value 13.278210
## final  value 13.278210 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 755.013483 
## iter  10 value 106.563338
## iter  20 value 72.415033
## iter  30 value 53.631323
## iter  40 value 35.409487
## iter  50 value 24.087262
## iter  60 value 21.658489
## iter  70 value 20.202606
## iter  80 value 19.468161
## iter  90 value 18.765293
## iter 100 value 18.566434
## final  value 18.566434 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1163.452596 
## iter  10 value 162.203173
## iter  20 value 114.996005
## iter  30 value 74.822474
## iter  40 value 57.482955
## iter  50 value 49.531921
## iter  60 value 45.598008
## iter  70 value 43.531337
## iter  80 value 39.784488
## iter  90 value 36.699233
## iter 100 value 35.333797
## final  value 35.333797 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 895.021866 
## iter  10 value 79.161582
## iter  20 value 50.166438
## iter  30 value 30.760110
## iter  40 value 26.161652
## iter  50 value 18.483924
## iter  60 value 17.147031
## iter  70 value 16.088299
## iter  80 value 15.597702
## iter  90 value 14.822788
## iter 100 value 13.881750
## final  value 13.881750 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 944.007098 
## iter  10 value 96.903825
## iter  20 value 57.153861
## iter  30 value 42.472189
## iter  40 value 31.775844
## iter  50 value 23.963262
## iter  60 value 20.207703
## iter  70 value 18.882541
## iter  80 value 18.288600
## iter  90 value 18.061890
## iter 100 value 17.801573
## final  value 17.801573 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1093.490284 
## iter  10 value 114.417283
## iter  20 value 68.173747
## iter  30 value 45.703719
## iter  40 value 38.486190
## iter  50 value 35.726268
## iter  60 value 34.437086
## iter  70 value 33.189011
## iter  80 value 32.384899
## iter  90 value 31.550402
## iter 100 value 28.151651
## final  value 28.151651 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 719.507944 
## iter  10 value 114.315383
## iter  20 value 63.843814
## iter  30 value 54.150679
## iter  40 value 48.117408
## iter  50 value 41.845353
## iter  60 value 39.994754
## iter  70 value 39.193198
## iter  80 value 38.831996
## iter  90 value 38.467315
## iter 100 value 38.110030
## final  value 38.110030 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 630.095309 
## iter  10 value 93.586476
## iter  20 value 52.027953
## iter  30 value 40.338482
## iter  40 value 36.307093
## iter  50 value 31.756862
## iter  60 value 28.726421
## iter  70 value 26.554785
## iter  80 value 24.841140
## iter  90 value 23.109059
## iter 100 value 22.328879
## final  value 22.328879 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 928.198957 
## iter  10 value 130.805188
## iter  20 value 84.589597
## iter  30 value 63.968292
## iter  40 value 54.021770
## iter  50 value 38.212580
## iter  60 value 34.721885
## iter  70 value 32.436929
## iter  80 value 31.003244
## iter  90 value 30.424261
## iter 100 value 29.884149
## final  value 29.884149 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1304.349600 
## iter  10 value 112.959939
## iter  20 value 70.077055
## iter  30 value 46.941840
## iter  40 value 34.005117
## iter  50 value 31.048815
## iter  60 value 29.482349
## iter  70 value 28.856504
## iter  80 value 27.999563
## iter  90 value 26.265306
## iter 100 value 25.574811
## final  value 25.574811 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1073.716285 
## iter  10 value 133.239664
## iter  20 value 82.062207
## iter  30 value 62.279932
## iter  40 value 41.859291
## iter  50 value 34.713375
## iter  60 value 31.547201
## iter  70 value 30.078904
## iter  80 value 29.395339
## iter  90 value 28.917715
## iter 100 value 28.591406
## final  value 28.591406 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1639.358558 
## iter  10 value 132.172499
## iter  20 value 83.643555
## iter  30 value 60.266653
## iter  40 value 45.953232
## iter  50 value 35.926615
## iter  60 value 29.208345
## iter  70 value 27.794532
## iter  80 value 26.768812
## iter  90 value 25.988168
## iter 100 value 25.546729
## final  value 25.546729 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 998.625720 
## iter  10 value 101.912434
## iter  20 value 72.161524
## iter  30 value 55.140353
## iter  40 value 45.783558
## iter  50 value 42.742015
## iter  60 value 38.824448
## iter  70 value 36.377273
## iter  80 value 34.695615
## iter  90 value 33.408278
## iter 100 value 31.235286
## final  value 31.235286 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 961.502334 
## iter  10 value 114.316850
## iter  20 value 74.136171
## iter  30 value 47.434494
## iter  40 value 38.735023
## iter  50 value 36.589796
## iter  60 value 35.453805
## iter  70 value 35.138065
## iter  80 value 34.547977
## iter  90 value 33.882320
## iter 100 value 33.352304
## final  value 33.352304 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1491.594765 
## iter  10 value 128.869241
## iter  20 value 95.890398
## iter  30 value 61.855665
## iter  40 value 49.354679
## iter  50 value 43.929104
## iter  60 value 42.344618
## iter  70 value 41.339485
## iter  80 value 40.648477
## iter  90 value 39.618470
## iter 100 value 39.270601
## final  value 39.270601 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 911.537762 
## iter  10 value 137.583927
## iter  20 value 70.688447
## iter  30 value 49.247039
## iter  40 value 43.362156
## iter  50 value 41.658621
## iter  60 value 40.832981
## iter  70 value 39.301138
## iter  80 value 38.539910
## iter  90 value 37.588164
## iter 100 value 37.039652
## final  value 37.039652 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 767.983643 
## iter  10 value 120.995155
## iter  20 value 67.664350
## iter  30 value 38.583566
## iter  40 value 30.004332
## iter  50 value 26.446612
## iter  60 value 23.536539
## iter  70 value 18.905759
## iter  80 value 16.030647
## iter  90 value 14.439559
## iter 100 value 13.510786
## final  value 13.510786 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1350.724326 
## iter  10 value 93.776190
## iter  20 value 60.505602
## iter  30 value 36.407394
## iter  40 value 21.929105
## iter  50 value 17.453708
## iter  60 value 15.595708
## iter  70 value 13.823681
## iter  80 value 11.427394
## iter  90 value 10.189145
## iter 100 value 9.195433
## final  value 9.195433 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1189.580459 
## iter  10 value 107.467515
## iter  20 value 63.430448
## iter  30 value 44.440917
## iter  40 value 29.907509
## iter  50 value 20.491041
## iter  60 value 18.259467
## iter  70 value 16.902734
## iter  80 value 16.363612
## iter  90 value 14.682151
## iter 100 value 13.337126
## final  value 13.337126 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 826.770650 
## iter  10 value 112.991863
## iter  20 value 78.459774
## iter  30 value 58.011463
## iter  40 value 43.801913
## iter  50 value 24.465343
## iter  60 value 18.246966
## iter  70 value 16.524251
## iter  80 value 15.519386
## iter  90 value 14.854299
## iter 100 value 14.433753
## final  value 14.433753 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 846.180272 
## iter  10 value 136.131544
## iter  20 value 94.738520
## iter  30 value 77.841376
## iter  40 value 53.800954
## iter  50 value 36.451298
## iter  60 value 32.535766
## iter  70 value 29.626652
## iter  80 value 28.122216
## iter  90 value 27.294724
## iter 100 value 26.464702
## final  value 26.464702 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1090.864780 
## iter  10 value 134.169370
## iter  20 value 94.611132
## iter  30 value 64.179415
## iter  40 value 45.457700
## iter  50 value 38.344423
## iter  60 value 35.378538
## iter  70 value 34.042474
## iter  80 value 32.978032
## iter  90 value 29.025328
## iter 100 value 26.706074
## final  value 26.706074 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 769.002019 
## iter  10 value 154.198478
## iter  20 value 88.024574
## iter  30 value 59.018324
## iter  40 value 49.594047
## iter  50 value 43.860999
## iter  60 value 42.489911
## iter  70 value 41.373164
## iter  80 value 39.858271
## iter  90 value 39.430721
## iter 100 value 38.670406
## final  value 38.670406 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1027.695034 
## iter  10 value 145.166115
## iter  20 value 77.568975
## iter  30 value 45.757080
## iter  40 value 27.739218
## iter  50 value 17.094858
## iter  60 value 14.754375
## iter  70 value 13.395958
## iter  80 value 12.529612
## iter  90 value 11.995174
## iter 100 value 11.569460
## final  value 11.569460 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1169.031629 
## iter  10 value 141.691605
## iter  20 value 93.568568
## iter  30 value 71.074642
## iter  40 value 58.377882
## iter  50 value 49.069332
## iter  60 value 40.093168
## iter  70 value 37.248583
## iter  80 value 35.004491
## iter  90 value 34.094539
## iter 100 value 33.692777
## final  value 33.692777 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1170.602413 
## iter  10 value 172.471459
## iter  20 value 107.941825
## iter  30 value 78.083110
## iter  40 value 64.816013
## iter  50 value 58.359677
## iter  60 value 55.289929
## iter  70 value 51.743900
## iter  80 value 49.886500
## iter  90 value 46.616962
## iter 100 value 45.456577
## final  value 45.456577 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 965.989908 
## iter  10 value 172.013367
## iter  20 value 107.353519
## iter  30 value 89.363993
## iter  40 value 76.758807
## iter  50 value 64.982900
## iter  60 value 58.354289
## iter  70 value 56.338859
## iter  80 value 55.080339
## iter  90 value 53.860817
## iter 100 value 52.057795
## final  value 52.057795 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 929.499272 
## iter  10 value 99.877682
## iter  20 value 77.619888
## iter  30 value 57.845545
## iter  40 value 37.677450
## iter  50 value 29.376630
## iter  60 value 26.326659
## iter  70 value 23.266297
## iter  80 value 20.438270
## iter  90 value 16.538779
## iter 100 value 15.042455
## final  value 15.042455 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1276.170019 
## iter  10 value 110.681194
## iter  20 value 83.904185
## iter  30 value 68.303510
## iter  40 value 57.393851
## iter  50 value 46.270687
## iter  60 value 41.564706
## iter  70 value 39.846059
## iter  80 value 38.576630
## iter  90 value 37.907422
## iter 100 value 36.753257
## final  value 36.753257 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1367.669452 
## iter  10 value 108.208756
## iter  20 value 71.889059
## iter  30 value 53.463527
## iter  40 value 42.478088
## iter  50 value 35.085574
## iter  60 value 31.976857
## iter  70 value 30.176894
## iter  80 value 26.367228
## iter  90 value 24.458461
## iter 100 value 23.237253
## final  value 23.237253 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1010.444679 
## iter  10 value 101.196339
## iter  20 value 67.026670
## iter  30 value 53.496210
## iter  40 value 47.815398
## iter  50 value 41.284363
## iter  60 value 39.049634
## iter  70 value 36.692077
## iter  80 value 35.185828
## iter  90 value 32.100893
## iter 100 value 31.107841
## final  value 31.107841 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 728.307278 
## iter  10 value 118.851750
## iter  20 value 71.706504
## iter  30 value 41.517378
## iter  40 value 31.121041
## iter  50 value 28.198797
## iter  60 value 27.106542
## iter  70 value 25.722929
## iter  80 value 23.137445
## iter  90 value 22.391222
## iter 100 value 21.554285
## final  value 21.554285 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 840.841986 
## iter  10 value 157.457664
## iter  20 value 77.688861
## iter  30 value 59.221517
## iter  40 value 51.959944
## iter  50 value 47.721366
## iter  60 value 46.518572
## iter  70 value 45.709918
## iter  80 value 45.343624
## iter  90 value 45.196954
## iter 100 value 45.040485
## final  value 45.040485 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 771.960600 
## iter  10 value 135.695836
## iter  20 value 82.732736
## iter  30 value 69.136384
## iter  40 value 62.474555
## iter  50 value 57.564031
## iter  60 value 55.368469
## iter  70 value 53.120715
## iter  80 value 52.251200
## iter  90 value 50.379937
## iter 100 value 48.304166
## final  value 48.304166 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1534.221770 
## iter  10 value 113.965221
## iter  20 value 76.826037
## iter  30 value 51.891328
## iter  40 value 37.470455
## iter  50 value 31.657367
## iter  60 value 28.846725
## iter  70 value 26.214874
## iter  80 value 25.115288
## iter  90 value 24.611550
## iter 100 value 24.350928
## final  value 24.350928 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 742.286701 
## iter  10 value 131.191025
## iter  20 value 79.574328
## iter  30 value 58.698497
## iter  40 value 41.090799
## iter  50 value 34.921539
## iter  60 value 30.595698
## iter  70 value 28.433807
## iter  80 value 26.117714
## iter  90 value 24.916474
## iter 100 value 23.943184
## final  value 23.943184 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1836.542380 
## iter  10 value 109.170194
## iter  20 value 57.045491
## iter  30 value 36.274499
## iter  40 value 29.565819
## iter  50 value 28.231776
## iter  60 value 22.678915
## iter  70 value 20.727495
## iter  80 value 19.142182
## iter  90 value 18.595766
## iter 100 value 18.222666
## final  value 18.222666 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 933.760678 
## iter  10 value 125.972571
## iter  20 value 69.290217
## iter  30 value 55.502799
## iter  40 value 43.469014
## iter  50 value 40.036376
## iter  60 value 38.185627
## iter  70 value 37.332460
## iter  80 value 35.948239
## iter  90 value 34.428907
## iter 100 value 32.830952
## final  value 32.830952 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 945.883524 
## iter  10 value 149.278132
## iter  20 value 111.738545
## iter  30 value 78.451778
## iter  40 value 63.196260
## iter  50 value 48.670886
## iter  60 value 44.867289
## iter  70 value 42.251689
## iter  80 value 40.829288
## iter  90 value 39.477729
## iter 100 value 38.915207
## final  value 38.915207 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 859.334608 
## iter  10 value 114.774434
## iter  20 value 79.963619
## iter  30 value 67.820126
## iter  40 value 60.542345
## iter  50 value 56.923529
## iter  60 value 54.547447
## iter  70 value 51.080436
## iter  80 value 47.358712
## iter  90 value 42.613835
## iter 100 value 39.830214
## final  value 39.830214 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 975.341757 
## iter  10 value 128.321132
## iter  20 value 86.572217
## iter  30 value 49.091642
## iter  40 value 38.415456
## iter  50 value 33.299747
## iter  60 value 31.594853
## iter  70 value 30.687922
## iter  80 value 28.888352
## iter  90 value 27.530958
## iter 100 value 26.966099
## final  value 26.966099 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 955.545518 
## iter  10 value 117.635201
## iter  20 value 85.936850
## iter  30 value 60.366845
## iter  40 value 42.481002
## iter  50 value 29.496924
## iter  60 value 25.934930
## iter  70 value 24.336603
## iter  80 value 23.608251
## iter  90 value 23.177811
## iter 100 value 22.617072
## final  value 22.617072 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 859.873254 
## iter  10 value 104.306802
## iter  20 value 55.167981
## iter  30 value 43.482665
## iter  40 value 36.040718
## iter  50 value 32.422034
## iter  60 value 31.237501
## iter  70 value 30.652867
## iter  80 value 30.267384
## iter  90 value 29.897241
## iter 100 value 29.748739
## final  value 29.748739 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1151.951855 
## iter  10 value 124.567826
## iter  20 value 75.044227
## iter  30 value 42.014264
## iter  40 value 29.563490
## iter  50 value 25.734536
## iter  60 value 22.800012
## iter  70 value 21.917350
## iter  80 value 21.157268
## iter  90 value 20.666322
## iter 100 value 20.290495
## final  value 20.290495 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1358.137205 
## iter  10 value 179.201581
## iter  20 value 113.289608
## iter  30 value 84.384391
## iter  40 value 66.244307
## iter  50 value 51.468837
## iter  60 value 46.362039
## iter  70 value 43.971264
## iter  80 value 42.361336
## iter  90 value 41.458540
## iter 100 value 40.397606
## final  value 40.397606 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 733.657555 
## iter  10 value 102.678178
## iter  20 value 59.977289
## iter  30 value 34.034233
## iter  40 value 22.103171
## iter  50 value 14.458051
## iter  60 value 12.371135
## iter  70 value 10.710288
## iter  80 value 9.065336
## iter  90 value 7.165871
## iter 100 value 5.846204
## final  value 5.846204 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 774.499031 
## iter  10 value 110.923521
## iter  20 value 69.446820
## iter  30 value 37.742493
## iter  40 value 23.002902
## iter  50 value 19.829243
## iter  60 value 18.883275
## iter  70 value 18.015924
## iter  80 value 17.431816
## iter  90 value 16.753207
## iter 100 value 16.195062
## final  value 16.195062 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 759.046402 
## iter  10 value 112.728319
## iter  20 value 70.166622
## iter  30 value 42.292826
## iter  40 value 25.203338
## iter  50 value 13.829150
## iter  60 value 8.007213
## iter  70 value 4.663812
## iter  80 value 3.966216
## iter  90 value 3.631321
## iter 100 value 3.263137
## final  value 3.263137 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 724.271411 
## iter  10 value 107.883803
## iter  20 value 54.581395
## iter  30 value 31.618682
## iter  40 value 16.069382
## iter  50 value 12.096206
## iter  60 value 11.384825
## iter  70 value 10.491024
## iter  80 value 9.612208
## iter  90 value 9.171526
## iter 100 value 8.628371
## final  value 8.628371 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 837.329912 
## iter  10 value 127.562002
## iter  20 value 79.525272
## iter  30 value 54.766585
## iter  40 value 30.088894
## iter  50 value 23.079181
## iter  60 value 21.263158
## iter  70 value 19.587461
## iter  80 value 18.443387
## iter  90 value 16.104577
## iter 100 value 14.836626
## final  value 14.836626 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1115.511549 
## iter  10 value 122.273436
## iter  20 value 65.387697
## iter  30 value 51.116442
## iter  40 value 42.648455
## iter  50 value 38.741595
## iter  60 value 34.221749
## iter  70 value 31.098805
## iter  80 value 26.712507
## iter  90 value 24.051676
## iter 100 value 23.085761
## final  value 23.085761 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1027.784742 
## iter  10 value 93.774779
## iter  20 value 49.619631
## iter  30 value 27.161296
## iter  40 value 13.576336
## iter  50 value 11.280569
## iter  60 value 10.784814
## iter  70 value 10.488353
## iter  80 value 10.300602
## iter  90 value 10.142218
## iter 100 value 9.925902
## final  value 9.925902 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 844.858092 
## iter  10 value 123.949406
## iter  20 value 71.699700
## iter  30 value 49.980986
## iter  40 value 42.261351
## iter  50 value 39.420324
## iter  60 value 37.538308
## iter  70 value 35.739813
## iter  80 value 33.818801
## iter  90 value 32.762115
## iter 100 value 32.272834
## final  value 32.272834 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 800.456239 
## iter  10 value 88.634855
## iter  20 value 53.650159
## iter  30 value 33.133687
## iter  40 value 21.945304
## iter  50 value 19.165351
## iter  60 value 17.215053
## iter  70 value 15.569437
## iter  80 value 14.968970
## iter  90 value 14.515175
## iter 100 value 14.180222
## final  value 14.180222 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 840.563647 
## iter  10 value 134.176163
## iter  20 value 101.023649
## iter  30 value 80.675980
## iter  40 value 67.370306
## iter  50 value 53.961224
## iter  60 value 44.365192
## iter  70 value 41.494654
## iter  80 value 40.533841
## iter  90 value 39.533751
## iter 100 value 38.632568
## final  value 38.632568 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1210.234708 
## iter  10 value 132.389361
## iter  20 value 79.635223
## iter  30 value 67.579159
## iter  40 value 55.628514
## iter  50 value 50.405837
## iter  60 value 37.217855
## iter  70 value 27.258107
## iter  80 value 23.859093
## iter  90 value 22.671857
## iter 100 value 22.028143
## final  value 22.028143 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 852.478637 
## iter  10 value 185.957833
## iter  20 value 98.716417
## iter  30 value 72.627192
## iter  40 value 62.389435
## iter  50 value 55.587293
## iter  60 value 50.988719
## iter  70 value 46.550925
## iter  80 value 44.434012
## iter  90 value 43.580725
## iter 100 value 42.940530
## final  value 42.940530 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 816.908351 
## iter  10 value 134.484827
## iter  20 value 102.807615
## iter  30 value 73.575576
## iter  40 value 47.322689
## iter  50 value 31.526156
## iter  60 value 27.718861
## iter  70 value 25.563092
## iter  80 value 24.047005
## iter  90 value 22.844585
## iter 100 value 22.123140
## final  value 22.123140 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 741.618044 
## iter  10 value 123.177986
## iter  20 value 79.863538
## iter  30 value 50.230049
## iter  40 value 29.107079
## iter  50 value 23.858140
## iter  60 value 17.528799
## iter  70 value 14.619176
## iter  80 value 13.551251
## iter  90 value 13.314444
## iter 100 value 13.154874
## final  value 13.154874 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 846.690747 
## iter  10 value 105.127587
## iter  20 value 57.448872
## iter  30 value 46.220912
## iter  40 value 36.982996
## iter  50 value 31.763133
## iter  60 value 27.667717
## iter  70 value 24.640130
## iter  80 value 23.251822
## iter  90 value 21.299082
## iter 100 value 18.695348
## final  value 18.695348 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 731.739940 
## iter  10 value 107.465636
## iter  20 value 69.512507
## iter  30 value 44.720141
## iter  40 value 24.550293
## iter  50 value 14.433048
## iter  60 value 10.586610
## iter  70 value 9.429045
## iter  80 value 8.678849
## iter  90 value 8.242896
## iter 100 value 7.966214
## final  value 7.966214 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 776.095111 
## iter  10 value 122.483876
## iter  20 value 84.383021
## iter  30 value 61.367553
## iter  40 value 46.770993
## iter  50 value 36.832989
## iter  60 value 33.613768
## iter  70 value 31.073527
## iter  80 value 29.720662
## iter  90 value 29.092456
## iter 100 value 28.350786
## final  value 28.350786 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1251.722744 
## iter  10 value 137.872808
## iter  20 value 91.660808
## iter  30 value 68.085672
## iter  40 value 58.439424
## iter  50 value 49.750983
## iter  60 value 46.440052
## iter  70 value 41.608244
## iter  80 value 40.817237
## iter  90 value 40.505015
## iter 100 value 40.152440
## final  value 40.152440 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1278.385335 
## iter  10 value 121.027259
## iter  20 value 67.655183
## iter  30 value 46.643204
## iter  40 value 36.746032
## iter  50 value 29.401185
## iter  60 value 25.622634
## iter  70 value 24.933154
## iter  80 value 23.813857
## iter  90 value 23.278364
## iter 100 value 23.003995
## final  value 23.003995 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 692.288556 
## iter  10 value 128.256415
## iter  20 value 82.085539
## iter  30 value 62.484035
## iter  40 value 55.339238
## iter  50 value 52.126104
## iter  60 value 50.363725
## iter  70 value 48.796693
## iter  80 value 46.063190
## iter  90 value 43.520389
## iter 100 value 42.279960
## final  value 42.279960 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 652.775751 
## iter  10 value 99.378971
## iter  20 value 57.183530
## iter  30 value 42.192476
## iter  40 value 36.272756
## iter  50 value 34.588252
## iter  60 value 33.650457
## iter  70 value 33.104900
## iter  80 value 32.660781
## iter  90 value 32.336350
## iter 100 value 32.092796
## final  value 32.092796 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 805.955605 
## iter  10 value 100.430863
## iter  20 value 65.262785
## iter  30 value 48.992571
## iter  40 value 30.838815
## iter  50 value 19.347329
## iter  60 value 16.363415
## iter  70 value 15.337531
## iter  80 value 13.718377
## iter  90 value 12.398038
## iter 100 value 10.290998
## final  value 10.290998 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 740.455161 
## iter  10 value 84.733860
## iter  20 value 62.468608
## iter  30 value 43.247724
## iter  40 value 37.029289
## iter  50 value 32.483983
## iter  60 value 26.862600
## iter  70 value 24.758712
## iter  80 value 22.903469
## iter  90 value 22.421556
## iter 100 value 21.826807
## final  value 21.826807 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1484.105353 
## iter  10 value 123.652850
## iter  20 value 72.295267
## iter  30 value 41.721534
## iter  40 value 29.333032
## iter  50 value 25.844663
## iter  60 value 24.168034
## iter  70 value 23.255630
## iter  80 value 22.731249
## iter  90 value 22.177385
## iter 100 value 21.048800
## final  value 21.048800 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 685.549013 
## iter  10 value 94.143507
## iter  20 value 58.802001
## iter  30 value 38.184590
## iter  40 value 26.328967
## iter  50 value 23.781130
## iter  60 value 21.295296
## iter  70 value 19.873051
## iter  80 value 19.022046
## iter  90 value 18.289589
## iter 100 value 16.498155
## final  value 16.498155 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 686.006745 
## iter  10 value 107.398397
## iter  20 value 52.842118
## iter  30 value 27.752727
## iter  40 value 20.015413
## iter  50 value 18.465519
## iter  60 value 17.823546
## iter  70 value 17.251858
## iter  80 value 16.982281
## iter  90 value 16.734737
## iter 100 value 16.561098
## final  value 16.561098 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1635.684481 
## iter  10 value 70.991439
## iter  20 value 33.163194
## iter  30 value 20.509614
## iter  40 value 13.063088
## iter  50 value 11.847084
## iter  60 value 11.368307
## iter  70 value 11.047674
## iter  80 value 9.095335
## iter  90 value 7.738106
## iter 100 value 6.842572
## final  value 6.842572 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 970.242041 
## iter  10 value 128.056365
## iter  20 value 83.205350
## iter  30 value 61.551260
## iter  40 value 44.804128
## iter  50 value 25.351303
## iter  60 value 16.900160
## iter  70 value 14.045674
## iter  80 value 12.493205
## iter  90 value 11.934168
## iter 100 value 11.603843
## final  value 11.603843 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 934.431400 
## iter  10 value 110.148626
## iter  20 value 73.312360
## iter  30 value 43.622068
## iter  40 value 28.802870
## iter  50 value 23.472215
## iter  60 value 21.856272
## iter  70 value 20.988328
## iter  80 value 20.554281
## iter  90 value 20.072491
## iter 100 value 19.799407
## final  value 19.799407 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1916.831215 
## iter  10 value 113.986688
## iter  20 value 91.571313
## iter  30 value 64.093314
## iter  40 value 51.357448
## iter  50 value 43.970001
## iter  60 value 40.581137
## iter  70 value 36.569706
## iter  80 value 31.910812
## iter  90 value 28.095541
## iter 100 value 26.058945
## final  value 26.058945 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 689.564445 
## iter  10 value 137.095301
## iter  20 value 67.240074
## iter  30 value 44.832767
## iter  40 value 33.440265
## iter  50 value 20.441586
## iter  60 value 16.839369
## iter  70 value 15.155907
## iter  80 value 12.515161
## iter  90 value 10.369242
## iter 100 value 9.310668
## final  value 9.310668 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 705.875513 
## iter  10 value 127.419625
## iter  20 value 74.359367
## iter  30 value 38.914190
## iter  40 value 22.835872
## iter  50 value 18.806960
## iter  60 value 17.461729
## iter  70 value 16.795280
## iter  80 value 16.261857
## iter  90 value 15.840665
## iter 100 value 15.521220
## final  value 15.521220 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 939.300225 
## iter  10 value 98.801687
## iter  20 value 65.270161
## iter  30 value 37.159234
## iter  40 value 22.188589
## iter  50 value 19.375101
## iter  60 value 18.519800
## iter  70 value 18.090096
## iter  80 value 17.685573
## iter  90 value 17.423244
## iter 100 value 17.173590
## final  value 17.173590 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 817.828435 
## iter  10 value 123.430171
## iter  20 value 80.547418
## iter  30 value 50.907271
## iter  40 value 39.807319
## iter  50 value 33.878975
## iter  60 value 31.328223
## iter  70 value 30.289481
## iter  80 value 29.545352
## iter  90 value 29.099476
## iter 100 value 28.879920
## final  value 28.879920 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 975.026493 
## iter  10 value 127.839084
## iter  20 value 76.609418
## iter  30 value 45.934857
## iter  40 value 32.444435
## iter  50 value 24.112157
## iter  60 value 17.813194
## iter  70 value 16.154892
## iter  80 value 15.039471
## iter  90 value 13.782909
## iter 100 value 13.015046
## final  value 13.015046 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1002.274236 
## iter  10 value 182.526404
## iter  20 value 118.677075
## iter  30 value 99.503281
## iter  40 value 82.854479
## iter  50 value 75.600683
## iter  60 value 66.316462
## iter  70 value 63.538243
## iter  80 value 61.580904
## iter  90 value 58.235522
## iter 100 value 56.053489
## final  value 56.053489 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 884.953677 
## iter  10 value 121.555119
## iter  20 value 55.689277
## iter  30 value 39.682138
## iter  40 value 31.779462
## iter  50 value 21.845434
## iter  60 value 19.657847
## iter  70 value 18.700435
## iter  80 value 17.956890
## iter  90 value 17.552435
## iter 100 value 16.257521
## final  value 16.257521 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1242.911533 
## iter  10 value 140.379707
## iter  20 value 89.869519
## iter  30 value 59.466649
## iter  40 value 35.823412
## iter  50 value 32.315454
## iter  60 value 28.881519
## iter  70 value 27.650469
## iter  80 value 24.454074
## iter  90 value 21.109346
## iter 100 value 20.033980
## final  value 20.033980 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1117.670887 
## iter  10 value 106.051677
## iter  20 value 49.985345
## iter  30 value 35.147247
## iter  40 value 26.280273
## iter  50 value 20.974160
## iter  60 value 18.958363
## iter  70 value 17.833195
## iter  80 value 15.461392
## iter  90 value 12.338972
## iter 100 value 11.214234
## final  value 11.214234 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 977.580939 
## iter  10 value 98.807783
## iter  20 value 66.515751
## iter  30 value 41.060825
## iter  40 value 26.888098
## iter  50 value 22.647100
## iter  60 value 18.292072
## iter  70 value 15.917123
## iter  80 value 14.730335
## iter  90 value 13.636750
## iter 100 value 11.164016
## final  value 11.164016 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 721.915109 
## iter  10 value 102.614474
## iter  20 value 72.822135
## iter  30 value 46.123930
## iter  40 value 36.545162
## iter  50 value 33.945586
## iter  60 value 32.745472
## iter  70 value 31.705419
## iter  80 value 28.474133
## iter  90 value 26.534402
## iter 100 value 22.011529
## final  value 22.011529 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1456.117191 
## iter  10 value 119.196743
## iter  20 value 78.101259
## iter  30 value 51.776931
## iter  40 value 32.428918
## iter  50 value 25.771807
## iter  60 value 23.478371
## iter  70 value 21.484476
## iter  80 value 20.761899
## iter  90 value 19.886399
## iter 100 value 19.447605
## final  value 19.447605 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1145.131937 
## iter  10 value 128.852071
## iter  20 value 92.161026
## iter  30 value 64.095128
## iter  40 value 54.075980
## iter  50 value 50.339618
## iter  60 value 44.341365
## iter  70 value 40.983495
## iter  80 value 37.801596
## iter  90 value 36.796359
## iter 100 value 35.708110
## final  value 35.708110 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 710.925998 
## iter  10 value 104.972487
## iter  20 value 58.820804
## iter  30 value 43.140447
## iter  40 value 40.354871
## iter  50 value 38.415615
## iter  60 value 37.172685
## iter  70 value 35.235502
## iter  80 value 31.862181
## iter  90 value 27.081978
## iter 100 value 23.678942
## final  value 23.678942 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 688.366574 
## iter  10 value 111.555443
## iter  20 value 76.299656
## iter  30 value 49.458356
## iter  40 value 33.746939
## iter  50 value 29.943777
## iter  60 value 25.952913
## iter  70 value 24.193265
## iter  80 value 22.958479
## iter  90 value 21.116093
## iter 100 value 20.675943
## final  value 20.675943 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1200.183847 
## iter  10 value 95.313223
## iter  20 value 54.681344
## iter  30 value 38.800580
## iter  40 value 28.736961
## iter  50 value 24.303761
## iter  60 value 21.933719
## iter  70 value 20.429373
## iter  80 value 19.588020
## iter  90 value 18.782642
## iter 100 value 17.720185
## final  value 17.720185 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1377.551614 
## iter  10 value 116.784299
## iter  20 value 80.401813
## iter  30 value 59.114034
## iter  40 value 43.933751
## iter  50 value 38.820006
## iter  60 value 33.799017
## iter  70 value 31.777224
## iter  80 value 30.902362
## iter  90 value 29.639048
## iter 100 value 28.472579
## final  value 28.472579 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1170.499960 
## iter  10 value 208.665083
## iter  20 value 161.408087
## iter  30 value 121.939530
## iter  40 value 108.079109
## iter  50 value 93.472908
## iter  60 value 78.598132
## iter  70 value 72.557188
## iter  80 value 68.877665
## iter  90 value 67.433786
## iter 100 value 64.655355
## final  value 64.655355 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 610.749440 
## iter  10 value 84.571058
## iter  20 value 45.658029
## iter  30 value 13.464824
## iter  40 value 8.229495
## iter  50 value 7.375350
## iter  60 value 6.592863
## iter  70 value 5.327142
## iter  80 value 4.401347
## iter  90 value 3.928416
## iter 100 value 3.613900
## final  value 3.613900 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 567.811156 
## iter  10 value 88.356850
## iter  20 value 57.436093
## iter  30 value 48.032355
## iter  40 value 37.311579
## iter  50 value 28.558229
## iter  60 value 24.641139
## iter  70 value 21.652453
## iter  80 value 17.332603
## iter  90 value 16.273340
## iter 100 value 15.307912
## final  value 15.307912 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 862.536820 
## iter  10 value 115.623766
## iter  20 value 57.350797
## iter  30 value 39.705021
## iter  40 value 32.893522
## iter  50 value 29.891843
## iter  60 value 28.127074
## iter  70 value 25.497308
## iter  80 value 21.826556
## iter  90 value 18.896095
## iter 100 value 16.585063
## final  value 16.585063 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 842.789151 
## iter  10 value 112.303464
## iter  20 value 54.972600
## iter  30 value 25.604243
## iter  40 value 9.588979
## iter  50 value 6.147195
## iter  60 value 5.372688
## iter  70 value 3.712201
## iter  80 value 3.284634
## iter  90 value 3.029432
## iter 100 value 2.854277
## final  value 2.854277 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 824.952208 
## iter  10 value 93.942633
## iter  20 value 50.027608
## iter  30 value 15.591496
## iter  40 value 7.276256
## iter  50 value 5.435698
## iter  60 value 4.706206
## iter  70 value 4.305903
## iter  80 value 4.013879
## iter  90 value 3.776400
## iter 100 value 3.606745
## final  value 3.606745 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1940.586577 
## iter  10 value 112.739030
## iter  20 value 64.444047
## iter  30 value 38.810530
## iter  40 value 26.553578
## iter  50 value 15.115238
## iter  60 value 11.336617
## iter  70 value 9.999602
## iter  80 value 9.100527
## iter  90 value 7.853471
## iter 100 value 7.530020
## final  value 7.530020 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 774.208030 
## iter  10 value 144.834209
## iter  20 value 91.187098
## iter  30 value 79.422368
## iter  40 value 73.688551
## iter  50 value 70.018323
## iter  60 value 66.641100
## iter  70 value 59.562532
## iter  80 value 53.278285
## iter  90 value 49.598729
## iter 100 value 45.722099
## final  value 45.722099 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 805.726964 
## iter  10 value 133.886849
## iter  20 value 82.160916
## iter  30 value 61.363131
## iter  40 value 54.321395
## iter  50 value 49.372203
## iter  60 value 45.174245
## iter  70 value 41.130452
## iter  80 value 40.023664
## iter  90 value 38.213378
## iter 100 value 37.049480
## final  value 37.049480 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 719.905968 
## iter  10 value 127.547069
## iter  20 value 92.850694
## iter  30 value 78.949523
## iter  40 value 72.934603
## iter  50 value 67.946854
## iter  60 value 61.908934
## iter  70 value 59.303117
## iter  80 value 56.301451
## iter  90 value 53.525314
## iter 100 value 51.793049
## final  value 51.793049 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1123.474406 
## iter  10 value 108.741811
## iter  20 value 60.993614
## iter  30 value 51.043630
## iter  40 value 47.679781
## iter  50 value 46.719674
## iter  60 value 42.263116
## iter  70 value 41.150027
## iter  80 value 40.881030
## iter  90 value 40.754346
## iter 100 value 40.710754
## final  value 40.710754 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 887.976516 
## iter  10 value 140.218582
## iter  20 value 81.057850
## iter  30 value 60.016911
## iter  40 value 51.479782
## iter  50 value 46.113686
## iter  60 value 44.554834
## iter  70 value 40.352567
## iter  80 value 39.168057
## iter  90 value 37.967781
## iter 100 value 36.746812
## final  value 36.746812 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1153.814173 
## iter  10 value 158.623607
## iter  20 value 104.933210
## iter  30 value 87.987257
## iter  40 value 79.094384
## iter  50 value 72.144498
## iter  60 value 68.229333
## iter  70 value 66.371337
## iter  80 value 65.294755
## iter  90 value 64.253520
## iter 100 value 63.731293
## final  value 63.731293 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 932.279385 
## iter  10 value 85.666244
## iter  20 value 37.881832
## iter  30 value 14.823605
## iter  40 value 3.859718
## iter  50 value 2.174565
## iter  60 value 1.968506
## iter  70 value 1.839730
## iter  80 value 1.786296
## iter  90 value 1.755230
## iter 100 value 1.726047
## final  value 1.726047 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 996.832802 
## iter  10 value 79.503552
## iter  20 value 47.233345
## iter  30 value 21.059769
## iter  40 value 12.741676
## iter  50 value 11.574312
## iter  60 value 10.700190
## iter  70 value 10.223247
## iter  80 value 9.454272
## iter  90 value 9.240277
## iter 100 value 8.926146
## final  value 8.926146 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 1357.402644 
## iter  10 value 69.701405
## iter  20 value 33.335903
## iter  30 value 15.339783
## iter  40 value 5.502410
## iter  50 value 2.899423
## iter  60 value 2.381366
## iter  70 value 2.044604
## iter  80 value 1.908371
## iter  90 value 1.796813
## iter 100 value 1.749417
## final  value 1.749417 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1163.695115 
## iter  10 value 107.097729
## iter  20 value 59.697209
## iter  30 value 31.614859
## iter  40 value 18.021113
## iter  50 value 11.732213
## iter  60 value 6.777037
## iter  70 value 4.161085
## iter  80 value 3.017966
## iter  90 value 2.570484
## iter 100 value 2.361752
## final  value 2.361752 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1001.535628 
## iter  10 value 129.507931
## iter  20 value 68.581157
## iter  30 value 34.810910
## iter  40 value 19.094587
## iter  50 value 14.875110
## iter  60 value 12.804052
## iter  70 value 11.707010
## iter  80 value 10.894926
## iter  90 value 7.143437
## iter 100 value 4.842953
## final  value 4.842953 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1489.905583 
## iter  10 value 104.094551
## iter  20 value 68.739603
## iter  30 value 49.469462
## iter  40 value 34.804062
## iter  50 value 24.814726
## iter  60 value 15.823334
## iter  70 value 12.325983
## iter  80 value 10.038910
## iter  90 value 8.892734
## iter 100 value 8.320424
## final  value 8.320424 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 898.951682 
## iter  10 value 114.298535
## iter  20 value 68.363576
## iter  30 value 39.197717
## iter  40 value 21.050613
## iter  50 value 15.589951
## iter  60 value 13.664363
## iter  70 value 12.311030
## iter  80 value 10.750577
## iter  90 value 10.278116
## iter 100 value 9.987362
## final  value 9.987362 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 672.707348 
## iter  10 value 106.359297
## iter  20 value 67.800989
## iter  30 value 40.746975
## iter  40 value 27.301432
## iter  50 value 22.311648
## iter  60 value 20.863007
## iter  70 value 19.361392
## iter  80 value 16.384897
## iter  90 value 14.227147
## iter 100 value 12.314283
## final  value 12.314283 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 683.756283 
## iter  10 value 141.835856
## iter  20 value 92.368599
## iter  30 value 74.311289
## iter  40 value 61.817844
## iter  50 value 56.721229
## iter  60 value 52.648806
## iter  70 value 50.648804
## iter  80 value 49.049133
## iter  90 value 48.038947
## iter 100 value 47.267209
## final  value 47.267209 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 875.637025 
## iter  10 value 125.354224
## iter  20 value 79.257867
## iter  30 value 57.461144
## iter  40 value 35.343814
## iter  50 value 20.992355
## iter  60 value 17.888774
## iter  70 value 15.706097
## iter  80 value 14.864244
## iter  90 value 14.515880
## iter 100 value 14.274596
## final  value 14.274596 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 911.695409 
## iter  10 value 173.736032
## iter  20 value 78.864149
## iter  30 value 54.965343
## iter  40 value 36.207268
## iter  50 value 22.123184
## iter  60 value 17.260058
## iter  70 value 14.505485
## iter  80 value 13.158879
## iter  90 value 12.161114
## iter 100 value 11.051777
## final  value 11.051777 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1267.902004 
## iter  10 value 141.042504
## iter  20 value 99.068495
## iter  30 value 81.562901
## iter  40 value 64.635065
## iter  50 value 54.476064
## iter  60 value 50.013682
## iter  70 value 48.385669
## iter  80 value 47.138472
## iter  90 value 45.101957
## iter 100 value 43.840018
## final  value 43.840018 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1154.100191 
## iter  10 value 112.016569
## iter  20 value 67.165853
## iter  30 value 42.055617
## iter  40 value 27.790011
## iter  50 value 24.628146
## iter  60 value 23.361786
## iter  70 value 22.506127
## iter  80 value 21.393918
## iter  90 value 20.806996
## iter 100 value 19.889128
## final  value 19.889128 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1146.858425 
## iter  10 value 95.505485
## iter  20 value 47.565928
## iter  30 value 33.970358
## iter  40 value 28.211063
## iter  50 value 25.085763
## iter  60 value 23.455459
## iter  70 value 21.988628
## iter  80 value 20.667916
## iter  90 value 19.022554
## iter 100 value 18.523736
## final  value 18.523736 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 980.663294 
## iter  10 value 118.314535
## iter  20 value 80.111219
## iter  30 value 65.222298
## iter  40 value 56.596098
## iter  50 value 53.938328
## iter  60 value 49.803268
## iter  70 value 47.953653
## iter  80 value 46.908759
## iter  90 value 46.225982
## iter 100 value 45.757848
## final  value 45.757848 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 729.770454 
## iter  10 value 103.698782
## iter  20 value 66.113972
## iter  30 value 31.825145
## iter  40 value 20.893140
## iter  50 value 17.902533
## iter  60 value 14.596533
## iter  70 value 13.414593
## iter  80 value 11.695185
## iter  90 value 10.162209
## iter 100 value 8.231707
## final  value 8.231707 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 687.276380 
## iter  10 value 129.284599
## iter  20 value 85.679688
## iter  30 value 64.756419
## iter  40 value 56.527490
## iter  50 value 52.390330
## iter  60 value 50.618219
## iter  70 value 49.495012
## iter  80 value 47.365495
## iter  90 value 46.161605
## iter 100 value 45.589344
## final  value 45.589344 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 962.737771 
## iter  10 value 160.443973
## iter  20 value 107.102251
## iter  30 value 79.270607
## iter  40 value 62.145554
## iter  50 value 50.862700
## iter  60 value 44.401342
## iter  70 value 41.169957
## iter  80 value 40.080139
## iter  90 value 38.964238
## iter 100 value 38.395458
## final  value 38.395458 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 747.445359 
## iter  10 value 101.409583
## iter  20 value 55.132631
## iter  30 value 35.137165
## iter  40 value 24.049569
## iter  50 value 21.129293
## iter  60 value 20.087061
## iter  70 value 19.168364
## iter  80 value 18.388976
## iter  90 value 17.677899
## iter 100 value 16.501509
## final  value 16.501509 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 729.394853 
## iter  10 value 130.358294
## iter  20 value 88.870822
## iter  30 value 45.066609
## iter  40 value 30.686133
## iter  50 value 25.262995
## iter  60 value 23.627448
## iter  70 value 22.121564
## iter  80 value 21.262267
## iter  90 value 20.569613
## iter 100 value 19.690617
## final  value 19.690617 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 769.887057 
## iter  10 value 250.691725
## iter  20 value 115.786269
## iter  30 value 74.352608
## iter  40 value 58.869532
## iter  50 value 48.395353
## iter  60 value 43.615909
## iter  70 value 41.475403
## iter  80 value 38.502683
## iter  90 value 37.610913
## iter 100 value 35.371732
## final  value 35.371732 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 739.545913 
## iter  10 value 122.137131
## iter  20 value 62.818913
## iter  30 value 35.361655
## iter  40 value 20.714145
## iter  50 value 17.385435
## iter  60 value 15.661885
## iter  70 value 15.091212
## iter  80 value 14.352896
## iter  90 value 13.968978
## iter 100 value 13.754662
## final  value 13.754662 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 832.266490 
## iter  10 value 135.473053
## iter  20 value 85.167117
## iter  30 value 45.354567
## iter  40 value 30.204455
## iter  50 value 26.252872
## iter  60 value 24.674807
## iter  70 value 23.902299
## iter  80 value 20.783121
## iter  90 value 18.655749
## iter 100 value 16.825341
## final  value 16.825341 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 945.721672 
## iter  10 value 155.545797
## iter  20 value 108.923274
## iter  30 value 77.956013
## iter  40 value 54.603048
## iter  50 value 40.022530
## iter  60 value 32.993727
## iter  70 value 31.265393
## iter  80 value 30.203203
## iter  90 value 29.392724
## iter 100 value 28.414600
## final  value 28.414600 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 1909.544089 
## iter  10 value 89.952604
## iter  20 value 45.680418
## iter  30 value 20.026754
## iter  40 value 8.450538
## iter  50 value 6.711918
## iter  60 value 5.744847
## iter  70 value 5.270611
## iter  80 value 4.802945
## iter  90 value 4.522376
## iter 100 value 4.366331
## final  value 4.366331 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1228.831718 
## iter  10 value 101.364904
## iter  20 value 34.327315
## iter  30 value 15.488030
## iter  40 value 6.859993
## iter  50 value 5.102470
## iter  60 value 4.277216
## iter  70 value 3.900847
## iter  80 value 3.768320
## iter  90 value 3.698184
## iter 100 value 3.505140
## final  value 3.505140 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 790.670866 
## iter  10 value 89.704971
## iter  20 value 47.928014
## iter  30 value 27.847463
## iter  40 value 20.144327
## iter  50 value 18.510185
## iter  60 value 16.439410
## iter  70 value 14.057668
## iter  80 value 12.100558
## iter  90 value 9.328826
## iter 100 value 7.803805
## final  value 7.803805 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 1094.445458 
## iter  10 value 92.878399
## iter  20 value 55.126762
## iter  30 value 40.410829
## iter  40 value 33.497273
## iter  50 value 27.582483
## iter  60 value 24.585911
## iter  70 value 21.994941
## iter  80 value 20.680279
## iter  90 value 19.499192
## iter 100 value 14.890174
## final  value 14.890174 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 1203.846570 
## iter  10 value 77.356188
## iter  20 value 36.738083
## iter  30 value 29.010759
## iter  40 value 26.293315
## iter  50 value 23.811808
## iter  60 value 22.312338
## iter  70 value 18.218925
## iter  80 value 17.029016
## iter  90 value 16.351007
## iter 100 value 12.172274
## final  value 12.172274 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 985.845976 
## iter  10 value 114.546227
## iter  20 value 67.416439
## iter  30 value 43.782150
## iter  40 value 31.692796
## iter  50 value 25.904118
## iter  60 value 23.945662
## iter  70 value 22.441549
## iter  80 value 21.943997
## iter  90 value 20.440112
## iter 100 value 19.702869
## final  value 19.702869 
## stopped after 100 iterations
## + Fold1: size=6, decay=0.001 
## # weights:  169
## initial  value 715.923104 
## iter  10 value 119.793042
## iter  20 value 71.565992
## iter  30 value 54.800901
## iter  40 value 43.250546
## iter  50 value 37.946321
## iter  60 value 32.617408
## iter  70 value 29.341663
## iter  80 value 27.331432
## iter  90 value 25.936076
## iter 100 value 25.361411
## final  value 25.361411 
## stopped after 100 iterations
## - Fold1: size=6, decay=0.001 
## + Fold2: size=6, decay=0.001 
## # weights:  169
## initial  value 1143.133598 
## iter  10 value 120.792246
## iter  20 value 70.063983
## iter  30 value 50.314463
## iter  40 value 37.543539
## iter  50 value 32.181351
## iter  60 value 29.215253
## iter  70 value 28.107129
## iter  80 value 27.689545
## iter  90 value 25.617763
## iter 100 value 24.581040
## final  value 24.581040 
## stopped after 100 iterations
## - Fold2: size=6, decay=0.001 
## + Fold3: size=6, decay=0.001 
## # weights:  169
## initial  value 711.153775 
## iter  10 value 114.895382
## iter  20 value 70.767249
## iter  30 value 45.043846
## iter  40 value 37.753008
## iter  50 value 33.635730
## iter  60 value 31.629516
## iter  70 value 30.403743
## iter  80 value 29.419789
## iter  90 value 28.578127
## iter 100 value 27.882937
## final  value 27.882937 
## stopped after 100 iterations
## - Fold3: size=6, decay=0.001 
## + Fold4: size=6, decay=0.001 
## # weights:  169
## initial  value 828.628104 
## iter  10 value 110.857985
## iter  20 value 51.189862
## iter  30 value 21.719964
## iter  40 value 12.469527
## iter  50 value 11.200246
## iter  60 value 10.541256
## iter  70 value 10.212894
## iter  80 value 10.027629
## iter  90 value 9.925835
## iter 100 value 9.855516
## final  value 9.855516 
## stopped after 100 iterations
## - Fold4: size=6, decay=0.001 
## + Fold5: size=6, decay=0.001 
## # weights:  169
## initial  value 936.848406 
## iter  10 value 105.675444
## iter  20 value 66.416936
## iter  30 value 49.041741
## iter  40 value 41.117142
## iter  50 value 37.767796
## iter  60 value 34.731607
## iter  70 value 32.901691
## iter  80 value 30.806288
## iter  90 value 29.668409
## iter 100 value 28.154290
## final  value 28.154290 
## stopped after 100 iterations
## - Fold5: size=6, decay=0.001 
## Aggregating results
## Fitting final model on full training set
## # weights:  169
## initial  value 1543.491617 
## iter  10 value 185.342493
## iter  20 value 108.712491
## iter  30 value 84.929788
## iter  40 value 74.268272
## iter  50 value 66.445090
## iter  60 value 63.500129
## iter  70 value 61.887138
## iter  80 value 60.429791
## iter  90 value 58.587768
## iter 100 value 56.085874
## final  value 56.085874 
## stopped after 100 iterations

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "threshold value",
        names = seq(0.05, 0.5, 0.05), col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] 0.2112964 0.2108795 0.2130263 0.2101084 0.2107545 0.2110046 0.2112130
##  [8] 0.2122551 0.2092955 0.2099416

We make the final prediction with the optimal threshold.

index = which.max(medians)
threshold = seq(0.05, 0.5, 0.05)[index]
nnProb = predict(nn.train, newdata=test_data, type="prob")
nnPred = rep("NOT.QSO", nrow(test_data))
nnPred[which(nnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(nnPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.2138356

We have a final economic profit of 0.2138356, which is good, but still not better than the decision tree model.

Variable importance and partial dependencies

We compute the variable importance to see which one is the more relevant in this model.

nn_imp <- varImp(nn.train, scale = F)
plot(nn_imp, scales = list(y = list(cex = .95)))

redshift and u are the more relevant variables.

partial(nn.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

partial(nn.train, pred.var = "u", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

We can see that the higher value in u the closer to 0 is the probability of being classified as a quasar object. The conclusion with redshift is the same as always.

Deep Neural Networks

Deep neural networks are a type of neural network architecture that consists of multiple hidden layers between the input and output layers. These networks are characterized by their depth, meaning they have more than one hidden layer, allowing them to learn complex and hierarchical representations of data. Deep learning, which relies on deep neural networks, has emerged as a powerful tool for solving a wide range of machine learning tasks, including image and speech recognition, natural language processing, and many others.

Deep neural networks have revolutionized many fields of artificial intelligence and have achieved remarkable success in tasks that were previously considered challenging, such as image recognition, speech understanding, and language translation. Their ability to automatically learn hierarchical representations from data has made them indispensable tools in modern machine learning and artificial intelligence research.

We use caret to compute it.

dnn.train <- train(class ~., 
                  method = "dnn", 
                  data = train_data,
                  preProcess = c("center", "scale"),
                  numepochs = 20, # number of iterations on the whole training set
                  tuneGrid = expand.grid(layer1 = 1:4,
                                         layer2 = 0:2,
                                         layer3 = 0:2,
                                         hidden_dropout = 0, 
                                         visible_dropout = 0),
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)

plot(dnn.train)

best_hyperparameters = dnn.train$bestTune

We change the threshold manually.

threshold = 0.4
dnnProb = predict(dnn.train, newdata=test_data, type="prob")
dnnPred = rep("NOT.QSO", nrow(test_data))
dnnPred[which(dnnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(dnnPred), test_data$class)$table

## Warning in confusionMatrix.default(factor(dnnPred), test_data$class): Levels
## are not in the same order for reference and data. Refactoring data to match.

profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit 

## [1] 0.01037673

We compute the most optimal one, because we want to improve our economic profit 0.0103767.

profit.i = matrix(NA, nrow = 15, ncol = 10)
# 20 replicates for training/testing sets for each of the 10 values of threshold

grid = best_hyperparameters

j <- 0
for (threshold in seq(0.25, 0.7, 0.05)){
  
  j <- j + 1
  #cat(j)
  for(i in 1:15){
    
    # partition data intro training (75%) and testing sets (25%)
    d <- createDataPartition(train_data$class, p = 0.4, list = FALSE)
    # select training sample
    
    train <- train_data[d,]
    test  <- train_data[-d,]  
    
    dnn.train <- train(class ~., 
                  method = "dnn", 
                  data = train,
                  preProcess = c("center", "scale"),
                  numepochs = 20, # number of iterations on the whole training set
                  tuneGrid = grid,
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)
    
    dnnProb = predict(dnn.train, test, type="prob")
    dnnPred = rep("NOT.QSO", nrow(test))
    dnnPred[which(dnnProb[,2] > threshold)] = "QSO"
    
    CM = confusionMatrix(factor(dnnPred), test$class)$table
    profit = sum(as.vector(CM)*profit.unit)/sum(CM)
    profit
    
    profit.i[i,j] <- profit
    
  }
}

# Threshold optimization:
boxplot(profit.i, main = "Threshold selection",
        ylab = "Economic profit",
        xlab = "Threshold value",
        names = seq(0.25, 0.7, 0.05), col="royalblue2",las=2)

# values around 0.2 are reasonable
medians = apply(profit.i, 2, median)
medians

##  [1] -0.03288870 -0.03288870  0.06140058  0.01031680  0.01031680  0.01031680
##  [7]  0.01031680  0.01031680  0.01031680  0.01031680

Final prediction with the optimal threshold.

index = which.max(medians)
threshold = seq(0.25, 0.7, 0.05)[index]
dnnProb = predict(dnn.train, newdata=test_data, type="prob")
dnnPred = rep("NOT.QSO", nrow(test_data))
dnnPred[which(dnnProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(dnnPred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit 

## [1] 0.1070762

Incredibly bad performance as a model.

Variable importance and partial dependencies

We study which are the most influential variables.

dnn_imp <- varImp(dnn.train, scale = F)
plot(dnn_imp, scales = list(y = list(cex = .95)))

Once again, redshift is the most important one. This time the increase in probability to be classified as quasar object increases constantly with the increase of this variable, up to 0.3610.

partial(dnn.train, pred.var = "redshift", which.class=2, plot = TRUE, prob=TRUE, rug = TRUE)

Model choosing

After all this model computing, we can see the best performing one is decision trees (economic profit = 0.2267878), followed by random forest (economic profit = 0.2245791) and then, support vector machines (economic profit = 0.2234289).

However, we have two more ideas that we are going to implement to see if we can improve them more: down-sampling and ensemble models.

Down-sampling

Downsampling is a term commonly used in the context of machine learning, especially in problems involving imbalanced classification. It involves reducing the size of the majority class sample to match that of the minority class sample. This is done by randomly selecting an appropriate number of samples from the majority class, so that its size matches that of the minority class.

When working with imbalanced datasets, where one class has significantly more samples than another, machine learning algorithms may be biased towards the majority class and struggle to recognize patterns in the minority class. This can result in models that do not generalize well and perform poorly in predicting the minority class.

By using downsampling, the aim is to balance the dataset, which can improve the model’s performance by enabling it to more effectively learn the characteristics of both classes. However, it’s important to note that downsampling also involves loss of information, as samples from the majority class are being removed. Therefore, striking an appropriate balance between reducing bias and retaining necessary information for the model to learn correctly is crucial.

We introduce in our control function the choice to downsample.

ctrl$sampling <- "up"

Now we are going to train our three best samples using this method and see if they improve.

SVM

svmFit <- train(class ~., method = "svmRadial", 
                data = train_data,
                preProcess = c("center", "scale"),
                tuneGrid = svmhyp,
                metric = "EconomicProfit", # maximizing the profit again
                trControl = ctrl)
print(svmFit)

## Support Vector Machines with Radial Basis Function Kernel 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3199, 3200, 3200, 3200, 3201 
## Addtional sampling using down-sampling prior to pre-processing
## 
## Resampling results:
## 
##   EconomicProfit
##   0.2173361     
## 
## Tuning parameter 'sigma' was held constant at a value of 0.02
## Tuning
##  parameter 'C' was held constant at a value of 0.75

Now we make the predictions and see if the economic profit improves.

svmProb = predict(svmFit, test_data, type="prob")
threshold = svmoptimal
svm.pred = rep("NOT.QSO", nrow(test_data)) # All good's
svm.pred[which(svmProb[,2] > threshold)] = "QSO" # Change the observations in the threshold as bad

CM = confusionMatrix(factor(svm.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2130855

Our profit before down-sampling was 0.2234289 and now our profit is 0.2130855, so it decreased. Let’s try to see if it improves the decision trees.

Decision trees

fit.c50 <- train(class ~.,
                data=train_data,
                method="C5.0",
                metric="EconomicProfit",
                tuneGrid = dthyp,
                trControl = ctrl)
fit.c50

## C5.0 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## No pre-processing
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3201, 3201, 3199, 3199, 3200 
## Addtional sampling using down-sampling
## 
## Resampling results:
## 
##   EconomicProfit
##   0.2246358     
## 
## Tuning parameter 'trials' was held constant at a value of 10
## Tuning
##  parameter 'model' was held constant at a value of tree
## Tuning
##  parameter 'winnow' was held constant at a value of TRUE

threshold = dtoptimal
Cred.pred = rep("NOT.QSO", nrow(test_data))
Cred.pred[which(c50.Prob[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(Cred.pred), test_data$class)$table
profit <- sum(profit.unit*CM)/sum(CM)
profit

## [1] 0.2267878

Our profit before down-sampling was 0.2267878 and now it is 0.2267878, which is the same economic impact. Lets’ take a look at our last of these 3 models.

Random forest

rf.train <- train(class ~., 
                  method = "rf", 
                  data = train_data,
                  preProcess = c("center", "scale"),
                  ntree = 200,
                  cutoff=c(0.7,0.3),
                  tuneGrid = rfhyp, 
                  metric = "EconomicProfit",
                  maximize = F,
                  trControl = ctrl)
rf.train

## Random Forest 
## 
## 4000 samples
##   15 predictor
##    2 classes: 'NOT.QSO', 'QSO' 
## 
## Pre-processing: centered (26), scaled (26) 
## Resampling: Cross-Validated (5 fold) 
## Summary of sample sizes: 3201, 3200, 3199, 3200, 3200 
## Addtional sampling using up-sampling prior to pre-processing
## 
## Resampling results:
## 
##   EconomicProfit
##   0.2293999     
## 
## Tuning parameter 'mtry' was held constant at a value of 8

Once trained, we obtain our predictions and the economic profit.

threshold = rfoptimal
rfProb = predict(rf.train, newdata=test_data, type="prob")
rfPred = rep("NOT.QSO", nrow(test_data))
rfPred[which(rfProb[,2] > threshold)] = "QSO"
CM = confusionMatrix(factor(rfPred), test_data$class)$table
best_profit = sum(as.vector(CM)*profit.unit)/sum(CM)
best_profit

## [1] 0.2292215

The economic profit before down-sampling was 0.2245791 and now it is 0.2292215, it increased

Hence, out best model yet is random forest with down-sampling. We use a final approach to try and improve it (ensemble models).

Ensemble models

Ensemble learning is a machine learning technique where multiple models are trained to solve the same problem, and their predictions are combined to make a final prediction. The idea behind ensemble learning is that by combining the predictions of multiple models, the overall performance can often be better than that of any individual model.

Ensemble learning can lead to improved generalization performance, robustness to overfitting, and better handling of complex datasets. It is widely used in various machine learning tasks, including classification, regression, and anomaly detection, and has been applied successfully in many real-world applications.

We are going our best 3 models: svm, decision trees and random forest before down-sampling. It is very computationally costly because we take economic profit into account.

First, we create an ensemble for classification with mode function.

mode <- function(v) {
  uniqv <- unique(v)
  uniqv[which.max(tabulate(match(v, uniqv)))]
}

Now, we make the predictions.

ensemble.pred = apply(data.frame(svm.pred, Cred.pred, rfPred), 1, mode) 
CM = confusionMatrix(factor(ensemble.pred), test_data$class)$table
profit = sum(as.vector(CM)*profit.unit)/sum(CM)
profit

## [1] 0.227863

Our best profit yet was that from random forest after down-sampling 0.2292215 and now we have 0.227863, so it did not improve it.

Final model and predictions.

After all this implementation of numerous machine learning tools and the implementation of various techniques to improve the models, we have arrived at our best model. Our best model is random forest with down-sampling and the corresponding economic profit of the predictions is 0.2292215.